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    Wyświetlanie 1-29 z 29
    Tytuł:
    Gas turbine vibration monitoring based on real data and neuro-fuzzy system
    Autorzy:
    Nail, Bachir
    Djaidir, Benrabeh
    Tibermacine, Imad Eddine
    Napoli, Christian
    Haidour, Nabil
    Abdelaziz, Rabehi
    Powiązania:
    https://bibliotekanauki.pl/articles/27313823.pdf
    Data publikacji:
    2024
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    vibration analysis
    gas turbine
    centrifugal compressor
    neuro-fuzzy system
    ANFIS
    Opis:
    The gas turbine is considered to be a very complex piece of machinery because of both its static structure and the dynamic behavior that results from the occurrence of vibration phenomena. It is required to adopt monitoring and diagnostic procedures for the identification and localization of vibration flaws in order to ensure the appropriate operation of large rotating equipment such as gas turbines. This is necessary in order to avoid catastrophic failures and deterioration and to ensure that proper operation occurs. Utilizing an approach that is based on spectrum analysis, the purpose of this study is to provide a model for the monitoring and diagnosis of vibrations in a GE MS3002 gas turbine and its driven centrifugal compressor. This will be done by utilizing the technique. Following that, the collection of vibration measurements for a model of the centrifugal compressor served as a suggestion for an additional method. This method is based on the neuro-fuzzy approach type ANFIS, and it aims to create an equivalent system that is able to make decisions without consulting a human being for the purpose of detecting vibratory defects. In spite of the fact that the compressor that was investigated has flaws, this procedure produced satisfactory results.
    Źródło:
    Diagnostyka; 2024, 25, 1; art. no. 202410
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An outlier-robust neuro-fuzzy system for classification and regression
    Autorzy:
    Siminski, Krzysztof
    Powiązania:
    https://bibliotekanauki.pl/articles/1838201.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    outliers
    neuro-fuzzy system
    clustering algorithm
    regression
    wyjątki
    system neurorozmyty
    algorytm grupowania
    Opis:
    Real life data often suffer from non-informative objects—outliers. These are objects that are not typical in a dataset and can significantly decline the efficacy of fuzzy models. In the paper we analyse neuro-fuzzy systems robust to outliers in classification and regression tasks. We use the fuzzy c-ordered means (FCOM) clustering algorithm for scatter domain partition to identify premises of fuzzy rules. The clustering algorithm elaborates typicality of each object. Data items with low typicalities are removed from further analysis. The paper is accompanied by experiments that show the efficacy of our modified neuro-fuzzy system to identify fuzzy models robust to high ratios of outliers.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 2; 303-319
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analysis of some problems of experimental mechanics and biomechanics by means the anfis neuro-fuzzy system
    Autorzy:
    Waszczyszyn, Z.
    Słoński, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/279788.pdf
    Data publikacji:
    2000
    Wydawca:
    Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
    Tematy:
    neuro-fuzzy system
    vibration of buldings
    proximal femurs
    fracture toughness
    experimental mechanics
    Opis:
    The Adaptive Neuro-Fuzzy Inference System (ANFIS) has been applied to the analysis of three problems: prediction of fundamental periods of vibrations of 5-storey prefabricated buildings, estimation of proximal femur strength, estimation of fracture toughness of dense concret. The results obtained by means of ANFIS are compared with those empirical formulae and forward neural networks. The ANFIS results have been proven to be superior.
    Analiza wybranych zagadnień doświadczalnej mechaniki i biomechaniki za pomocą neuro-rozmytego systemu ANFIS. Adaptacyjny neuro-rozmyty system ANFIS został zastosowany do analizy trzech problemów: określenie podstawowych okresów drgań 5-piętrowych budynków prefabrykowanych, określenie wytrzymałości górnej części kości udowych oraz oszacowanie odporności na zniszczenie betonów ciężkich. Wyniki otrzymane za pomocą systemu ANFIS porównano z wynikami, jakie dają wzory empiryczne i jednokierunkowe sieci neuronowe. Wykazano, że najlepszą dokładność daje system ANFIS.
    Źródło:
    Journal of Theoretical and Applied Mechanics; 2000, 38, 2; 429-445
    1429-2955
    Pojawia się w:
    Journal of Theoretical and Applied Mechanics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparison of incomplete data handling techniques for neuro-fuzzy systems
    Autorzy:
    Sikora, M.
    Simiński, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/305722.pdf
    Data publikacji:
    2014
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    incomplete data
    marginalization
    imputation
    neuro-fuzzy system
    ANNBFIS
    PDS
    IFCM
    OCS
    NPS
    Opis:
    Real-life data sets sometimes miss some values. The incomplete data needs specialized algorithms or preprocessing that allows the use of the algorithms for complete data. The paper presents a comparison of various techniques for handling incomplete data in the neuro-fuzzy system ANNBFIS. The crucial procedure in the creation of a fuzzy model for the neuro-fuzzy system is the partition of the input domain. The most popular approach (also used in the ANNBFIS) is clustering. The analyzed approaches for clustering incomplete data are: preprocessing (marginalization and imputation) and specialized clustering algorithms (PDS, IFCM, OCS, NPS). The objective of our research is the comparison of the preprocessing techniques and specialized clustering algorithms to find the the most-advantageous technique for handling incomplete data with a neuro-fuzzy system. This approach is also the indirect validation of clustering.
    Źródło:
    Computer Science; 2014, 15 (4); 441-458
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neuro-fuzzy control of a robotic manipulator
    Autorzy:
    Gierlak, P.
    Muszyńska, M.
    Żylski, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/955199.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    manipulator
    sztuczna inteligencja
    sieci neuronowe
    robot
    robotic manipulator
    force control
    neuro-fuzzy system
    Opis:
    In this paper, to solve the problem of control of a robotic manipulator’s movement with holonomical constraints, an intelligent control system was used. This system is understood as a hybrid controller, being a combination of fuzzy logic and an artificial neural network. The purpose of the neuro-fuzzy system is the approximation of the nonlinearity of the robotic manipulator’s dynamic to generate a compensatory control. The control system is designed in such a way as to permit modification of its properties under different operating conditions of the two-link manipulator.
    Źródło:
    International Journal of Applied Mechanics and Engineering; 2014, 19, 3; 575-584
    1734-4492
    2353-9003
    Pojawia się w:
    International Journal of Applied Mechanics and Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparison of Fuzzy System with Neural Aggregation FSNA with classical TSK fuzzy system in anti-collision problem of USV
    Autorzy:
    Szymak, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/260160.pdf
    Data publikacji:
    2017
    Wydawca:
    Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
    Tematy:
    neuro-fuzzy system
    neural aggregation of fuzzy rules
    cooperative co-evolution
    anti-collision of USV
    Opis:
    The paper presents the research whose the main goal was to compare a new Fuzzy System with Neural Aggregation of fuzzy rules FSNA with a classical Takagi-Sugeno-Kanga TSK fuzzy system in an anti-collision problem of Unmanned Surface Vehicle USV. Both systems the FSNA and the TSK were learned by means of Cooperative Co-evolutionary Genetic Algorithm with Indirect Neural Encoding CCGA-INE. The paper includes an introduction to the subject, a description of the new FSNA and the tuning method CCGA-INE, and at the end, numerical research results with a summary. The research includes comparison of the FSNA with the classical TSK system in the anti-collision problem of the USV.
    Źródło:
    Polish Maritime Research; 2017, 3; 3-14
    1233-2585
    Pojawia się w:
    Polish Maritime Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    GrNFS: A granular neuro-fuzzy system for regression in large volume data
    Autorzy:
    Siminski, Krzysztof
    Powiązania:
    https://bibliotekanauki.pl/articles/2055169.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    granular computing
    neuro-fuzzy system
    large volume data
    machine learning
    przetwarzanie ziarniste
    system neurorozmyty
    uczenie maszynowe
    Opis:
    Neuro-fuzzy systems have proved their ability to elaborate intelligible nonlinear models for presented data. However, their bottleneck is the volume of data. They have to read all data in order to produce a model. We apply the granular approach and propose a granular neuro-fuzzy system for large volume data. In our method the data are read by parts and granulated. In the next stage the fuzzy model is produced not on data but on granules. In the paper we introduce a novel type of granules: a fuzzy rule. In our system granules are represented by both regular data items and fuzzy rules. Fuzzy rules are a kind of data summaries. The experiments show that the proposed granular neuro-fuzzy system can produce intelligible models even for large volume datasets. The system outperforms the sampling techniques for large volume datasets.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 3; 445--459
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modelowanie dwurotorowego systemu aerodynamicznego z wykorzystaniem systemu neurorozmytego
    Modeling of Two Rotor Aerodynamical System Using the Neuro-Fuzzy System
    Autorzy:
    Woźnica, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/277571.pdf
    Data publikacji:
    2014
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    TRAS
    dwurotorowy system aerodynamiczny
    system neurorozmyty
    estymacja stanu
    Two Rotor Aerodynamical System
    neuro-fuzzy system
    state estimation
    ANFIS
    Opis:
    W artykule przedstawiono propozycję modelu neurorozmytego dla złożonego obiektu nieliniowego. Ze względu na osobliwości modeli analitycznych, zasugerowano strukturę rozmytą z adaptacyjnym doborem parametrów. Opracowano koncepcję adaptacyjnego obserwatora rozmytego, działającego na podstawie stworzonego modelu neurorozmytego. Dokonano oceny efektywności modelu i estymatora adaptacyjnego pod względem złożoności konstrukcji i nakładu obliczeniowego. Procedura implementacji modelu została przeprowadzona z użyciem środowiska obliczeniowego MATLAB.
    The paper presents a proposal neurofuzzy model for complex nonlinear plant. Due to the peculiarities of analytical models, suggested fuzzy structure with adaptive selection of parameters. The concept of adaptive fuzzy observer, operating on the basis of created of neurofuzzy model. An evaluation of the effectiveness of the model and adaptive estimator in terms of the complexity of the design and computational effort has been made. Implementations of the model were carried out based on MATLAB environment tools.
    Źródło:
    Pomiary Automatyka Robotyka; 2014, 18, 6; 86-91
    1427-9126
    Pojawia się w:
    Pomiary Automatyka Robotyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Forecasting the Flow Coefficient of the River Basin Using Adaptive Fuzzy Inference System and Fuzzy SMRGT Method
    Autorzy:
    Gunal, Ayse Yeter
    Mehdi, Ruya
    Powiązania:
    https://bibliotekanauki.pl/articles/27323840.pdf
    Data publikacji:
    2023
    Wydawca:
    Polskie Towarzystwo Inżynierii Ekologicznej
    Tematy:
    ANFIS
    adaptive neuro-fuzzy inference system
    SMRGT
    flow coefficient
    fuzzy logic
    surface water
    Opis:
    In hydrology and water resources engineering, predicting the flow coefficient is a crucial task that helps estimate the precipitation resulting in a surface flow. Accurate flow coefficient prediction is essential for efficient water management, flood control strategy development, and water resource planning. This investigation calculated the flow coefficient using models based on Simple Membership functions and fuzzy Rules Generation Technique (SMRGT) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The fuzzy logic methods are used to model the intricate connections between the inputs and the output. Statistical parameters such as the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) were used to evaluate the performance of models. The statistical tests outcome for the SMRGT model was (RMSE:0.056, MAE:1.92, MAPE:6.88, R2:0.996), and for the ANFIS was (RMSE:0.96, MAE:2.703, MAPE:19.97, R2:0.8038). According to the findings, the SMRGT, a physics-based model, exhibited superior accuracy and reliability in predicting the flow coefficient compared to ANFIS. This is attributed to the SMRGT’s ability to integrate expert knowledge and domain-specific information, rendering it a viable solution for diverse issues.
    Źródło:
    Journal of Ecological Engineering; 2023, 24, 7; 96--107
    2299-8993
    Pojawia się w:
    Journal of Ecological Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A hybrid cascade neuro-fuzzy network with pools of extended neo-fuzzy neurons and its deep learning
    Autorzy:
    Bodyanskiy, Yevgeniy V.
    Tyshchenko, Oleksii K.
    Powiązania:
    https://bibliotekanauki.pl/articles/330840.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    data stream
    membership function
    training procedure
    adaptive neuro-fuzzy system
    extended neo-fuzzy neuron
    strumień danych
    funkcja przynależności
    neuronowo rozmyty układ adaptacyjny
    Opis:
    This research contribution instantiates a framework of a hybrid cascade neural network based on the application of a specific sort of neo-fuzzy elements and a new peculiar adaptive training rule. The main trait of the offered system is its competence to continue intensifying its cascades until the required accuracy is gained. A distinctive rapid training procedure is also covered for this case that offers the possibility to operate with non-stationary data streams in an attempt to provide online training of multiple parametric variables. A new training criterion is examined for handling non-stationary objects. Additionally, there is always an occasion to set up (increase) the inference order and the number of membership relations inside the extended neo-fuzzy neuron.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2019, 29, 3; 477-488
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A new intelligentapproach in predictive maintenance of separation system
    Autorzy:
    Marichal, G. N.
    Ávila, D.
    Hernández, A.
    Padrón, I.
    Powiązania:
    https://bibliotekanauki.pl/articles/116306.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    marine fuel separators
    separation system
    predictive maintenance
    greenhouse gas (GHG)
    Fast Fourier Transformation (FFT)
    genetic neuro-fuzzy system
    genetic algorithm
    supervised learning
    Opis:
    Reducing contaminant emissions is an important task of any industry, included the maritime one. In fact, in April 2018, IMO (International Maritime Organization) adopted an Initial Strategy on reduction of Greenhouse gas (GHG) emissions from ships. An essential part responsible for producing these emissions is the diesel engine. For that reason vessels include separation systems for heavy fuel oils. The purpose of this work is to improve the predictive maintenance techniques incorporating new intelligent approaches. An analysis of vibrations of this separation system was made and their characteristics were used in a Genetic Neuro-Fuzzy System in order to design an intelligent maintenance based on condition monitoring. The achieved results show that the proposed method provides an improvement since it indicates if a maintenance operation is necessary before the schedule one or if it could be possible extend the next maintenance service.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2020, 14, 2; 385-390
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction of littoral drift with adaptive neuro-fuzzy inference system
    Ocena dryfu morskiego z wykorzystaniem systemu ANFIS [Adaptive Neuro-Fuzzy Inference System]
    Autorzy:
    Sabet, M S
    Naseri, M.A.
    Sabet, H.S.
    Powiązania:
    https://bibliotekanauki.pl/articles/81613.pdf
    Data publikacji:
    2010
    Wydawca:
    Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
    Tematy:
    littoral sand drift
    coastal zone
    adaptive neuro-fuzzy inference system
    validation
    physical process
    database
    Opis:
    The amount of sand moving parallel to a coastline forms a prerequisite for many harbor design projects. Such information is currently obtained through various empirical formulae. Despite so many works in the past, an accurate and reliable estimation of the rate of sand drift has still remained a problem. It is a non-linear process and can be described by chaotic time-series. The current study addresses this issue through the use of Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is about taking an initial fuzzy inference system (FIS) and tuning it with a back propagation algorithm based on the collection of input-output data. ANFIS was developed to predict the sand drift from a variety of causative variables. The structure and algorithm of ANFIS for predicting the rate of sand drift is described. The Adaptive Neuro-Fuzzy Inference System was validated by confi rming its consistency with a database of specifi ed physical process.
    W artykule przedstawiono adaptację systemu ANFIS do oceny wielkości dryfu fal piaskowych poruszających się wzdłuż wybrzeża morskiego. Pomimo wielu informacji o charakterze ilościowym oraz jakościowym zebranych w badaniach terenowych oraz opracowanych wzorów empirycznych opisujących analizowane zjawisko, autorzy widzą potrzebę stosowania symulacji zjawiska za pomocą metod numerycznych. Takie możliwości daje omówiony w pracy system ANFIS.
    Źródło:
    Annals of Warsaw University of Life Sciences - SGGW. Land Reclamation; 2010, 42, 1; 159-167
    0208-5771
    Pojawia się w:
    Annals of Warsaw University of Life Sciences - SGGW. Land Reclamation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    New Methods for Designing and Reduction of Neuro-Fuzzy Systems
    Autorzy:
    Cpałka, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/108790.pdf
    Data publikacji:
    2010
    Wydawca:
    Społeczna Akademia Nauk w Łodzi
    Tematy:
    algorithm of best global eliminations
    algorithm of best local eliminations
    consecutive eliminations algorithm
    consecutive mergings algorithm
    interpretability
    logical approach
    neuro-fuzzy system
    Opis:
    In the paper, we propose novel methods for designing and reduction of neuro-fuzzy systems without the deterioration of their accuracy. The reduction and merging algorithms gradually eliminate inputs, rules, antecedents, and the number of discretization points of integrals in the center of area defuzzification method. Our algorithms have been tested using well known classification benchmark.
    Źródło:
    Journal of Applied Computer Science Methods; 2010, 2 No. 2; 113-126
    1689-9636
    Pojawia się w:
    Journal of Applied Computer Science Methods
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence
    Autorzy:
    Li, C.
    Chiang, T. W.
    Powiązania:
    https://bibliotekanauki.pl/articles/331280.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    zbiór rozmyty
    system neuronowo-rozmyty
    optymalizacja rojem cząstek
    szereg czasowy
    complex fuzzy set
    complex neuro fuzzy system
    hierarchical multi swarm
    particle swarm optimization (PSO)
    recursive least squares estimator
    time series forecasting
    Opis:
    Financial investors often face an urgent need to predict the future. Accurate forecasting may allow investors to be aware of changes in financial markets in the future, so that they can reduce the risk of investment. In this paper, we present an intelligent computing paradigm, called the Complex Neuro-Fuzzy System (CNFS), applied to the problem of financial time series forecasting. The CNFS is an adaptive system, which is designed using Complex Fuzzy Sets (CFSs) whose membership functions are complex-valued and characterized within the unit disc of the complex plane. The application of CFSs to the CNFS can augment the adaptive capability of nonlinear functional mapping, which is valuable for nonlinear forecasting. Moreover, to optimize the CNFS for accurate forecasting, we devised a new hybrid learning method, called the HMSPSO-RLSE, which integrates in a hybrid way the so-called Hierarchical Multi-Swarm PSO (HMSPSO) and the well known Recursive Least Squares Estimator (RLSE). Three examples of financial time series are used to test the proposed approach, whose experimental results outperform those of other methods.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 787-800
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Zastosowanie ANFIS w analizie wyników badań gruntów
    Application of the ANFIS to analysis of results from soil testings
    Autorzy:
    Daniszewska, E
    Powiązania:
    https://bibliotekanauki.pl/articles/391234.pdf
    Data publikacji:
    2014
    Wydawca:
    Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej
    Tematy:
    adaptacyjny system neuronowo-rozmyty
    logika rozmyta
    trójosiowe badanie gruntu
    prędkość ścinania
    adaptive neuro-fuzzy inference system
    fuzzy logic
    soil triaxial testing
    shear speed
    Opis:
    Adaptacyjny system wnioskowania neuronowo-rozmytego ANFIS (Adaptive Neuro-Fuzzy Inference System) w programie Matlab posłużył modelowaniu i określaniu relacji między prędkością ścinania a parametrami wytrzymałościowymi gruntu. Sprawdzono możliwości i umiejętności narzędzia ANFIS w interpretacji wyników badań trójosiowego ściskania iłów pobranych z okolic Olsztyna. Model neuronowo-rozmyty został zbudowany na podstawie zbioru wartości, którymi dysponowano po szeregu badań eksperymentalnych, łącznie z wartościami parametrów wytrzymałościowych gruntu na ścinanie. Baza danych wykorzystana do modelowania neuronowo-rozmytego składa się z 6 różnych parametrów gruntowych dla każdej z 12 prędkości ścinania stosowanych podczas badań trójosiowych. Umiejętność uczenia zweryfikowano na bazie danych testowych - model neuronowo-rozmyty zbudowany został z zestawów szkoleniowych, a dokładność została zweryfikowana przez zestawy testów, z którymi model miał do czynienia po raz pierwszy. Wyniki z modelu ANFIS nie odbiegały znacznie od tych, które zostały uzyskane bezpośrednio z badań fizycznych. System ANFIS okazał się narzędziem niezwykle uniwersalnym i nieskomplikowanym w obsłudze. Pozwolił uwzględnić wieloaspektowość wzajemnych relacji parametrów gruntowych.
    The article was analyzed in order to test applicability and capability of the ANFIS tool used for interpretation of results of triaxial shear tests on loamy soils sampled near Olsztyn. The ANFIS system in the Matlab software programme was used to model and determine relationships between the shear stress and soil resistance parameters in a triaxial shear test apparatus. It has been demonstrated that the achieved shear strength parameters are significantly affected by the variables tested during the triaxial experiments and physical parameters of a given soil sample, but also by the loading increment rate during the tests. It is extremely important to adjust the rate of loading during a test according to the preliminary characterization of a tested ground sample so as to have some control over the obtained ground strength parameters. The neuro-fuzzy model has been constructed based on a set of values obtained after a series of experimental tests, including values of ground shear strength parameters. The database used for the neuro-fuzzy modelling consisted of 6 different ground parameters for each of the 12 shear stress rates applied during the triaxial tests. The learnability was verified on a database composed of the test results – a neuro-fuzzy model was built from learning sets and its accuracy was verified by sets of tests to which the model was applied for the first time. The results obtained from the ANFIS model did not diverge substantially from the ones obtained directly by performing the physical tests. The ANFIS proved to be highly universal and easy to operate. It accounted for the multi-faceted nature of interrelationships between ground parameters.
    Źródło:
    Budownictwo i Architektura; 2014, 13, 2; 7-15
    1899-0665
    Pojawia się w:
    Budownictwo i Architektura
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modelling of Plastic Flow Behaviour of Metals in the Hot Deformation Process Using Artificial Intelligence Methods
    Autorzy:
    Mrzygłód, Barbara
    Łukaszek-Sołek, Aneta
    Olejarczyk-Wożeńska, Izabela
    Pasierbiewicz, Karolina
    Powiązania:
    https://bibliotekanauki.pl/articles/2174622.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    hot deformation
    Inconel 718
    rheological model
    forming process
    neuro-fuzzy inference system
    odkształcanie na gorąco
    model reologiczny
    proces formowania
    Opis:
    Hot deformation of metals is a widely used process to produce end products with the desired geometry and required mechanical properties. To properly design the hot forming process, it is necessary to examine how the tested material behaves during hot deformation. Model studies carried out to characterize the behaviour of materials in the hot deformation process can be roughly divided into physical and mathematical simulation techniques. The methodology proposed in this study highlights the possibility of creating rheological models for selected materials using methods of artificial intelligence, such as neuro-fuzzy systems. The main goal of the study is to examine the selected method of artificial intelligence to know how far it is possible to use this method in the development of a predictive model describing the flow of metals in the process of hot deformation. The test material was Inconel 718 alloy, which belongs to the family of austenitic nickel-based superalloys characterized by exceptionally high mechanical properties, physicochemical properties and creep resistance. This alloy is hardly deformable and requires proper understanding of the constitutive behaviour of the material under process conditions to directly enable the optimization of deformability and, indirectly, the development of effective shaping technologies that can guarantee obtaining products with the required microstructure and desired final mechanical properties. To be able to predict the behaviour of the material under non-experimentally tested conditions, a rheological model was developed using the selected method of artificial intelligence, i.e. the Adaptive Neuro-Fuzzy Inference System (ANFIS). The source data used in these studies comes from a material experiment involving compression of the tested alloy on a Gleeble 3800 thermo-mechanical simulator at temperatures of 900, 1000, 1050, 1100, 1150oC with the strain rates of 0.01 - 100 s-1 to a constant true strain value of 0.9. To assess the ability of the developed model to describe the behaviour of the examined alloy during hot deformation, the values of yield stress determined by the developed model (ANFIS) were compared with the results obtained experimentally. The obtained results may also support the numerical modelling of stress-strain curves.
    Źródło:
    Archives of Foundry Engineering; 2022, 22, 3; 41--52
    1897-3310
    2299-2944
    Pojawia się w:
    Archives of Foundry Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction of lightning density value tower based on Adaptive Neuro-fuzzy Inference System
    Autorzy:
    Said, Sri Mawar
    Nappu, Muhammad Bachtiar
    Asri, Andarini
    Utomo, Bayu Tri
    Powiązania:
    https://bibliotekanauki.pl/articles/1841146.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    adaptive neuro-fuzzy inference system
    lightning density prediction tower
    Transmission Line Arrester
    adaptacyjny system wnioskowania neurorozmytego
    wieża prognozowania gęstości piorunów
    ogranicznik linii transmisyjnej
    Opis:
    Lightning is one of the causes of transmission disorders and natural phenomena that cannot be avoided. The South Sulawesi region is located close to the equator and has a high lightning density. This condition results in lightning susceptibility of distur- bances to electrical system lines, especially in high-voltage airlines and substations. An Adaptive Neuro-Fuzzy Inference System (ANFIS) will show the Root Mean Square Error (RMSE) based on the membership function type. This journal is to predict the value of the transmission tower lightning density using the ANFIS method. The value of the lightning strike density index can later be determined based on ANFIS predictions. Analysis of the value calculation system of structural lightning strikes in the South Sulawesi region of the Sungguminasa-Tallasa route can be categorized as three characteristics lightning density (Nd). The calculation system results for the value of structural lightning struck in the South Sulawesi region and validated between manual calculations and ANFIS with an average percentage of 0.0554%.
    Źródło:
    Archives of Electrical Engineering; 2021, 70, 3; 499-511
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Robustifying analysis of the direct adaptive control of unknown multivariable nonlinear systems based on a new neuro-fuzzy method
    Autorzy:
    Theodoridis, D. C.
    Boutalis, Y.S.
    Christodoulou, M. A.
    Powiązania:
    https://bibliotekanauki.pl/articles/91598.pdf
    Data publikacji:
    2011
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    nonlinear systems
    control
    neuro-fuzzy dynamical system
    fuzzy systems
    FS
    fuzzy recurrent high order neural network
    F-RHONN
    adaptive regulator
    parameter
    “Hopping”
    “Modified Hopping”
    modeling errors
    asymptotic regulation
    Opis:
    In this paper, we are dealing with the problem of directly regulating unknown multivariable affine in the control nonlinear systems and its robustness analysis. The method employs a new Neuro-Fuzzy Dynamical System definition, which uses the concept of Fuzzy Systems (FS) operating in conjunction with High Order Neural Networks. In this way the unknown plant is modeled by a fuzzy - recurrent high order neural network structure (F-RHONN), which is of the known structure considering the neglected nonlinearities. The development is combined with a sensitivity analysis of the closed loop in the presence of modeling imperfections and provides a comprehensive and rigorous analysis showing that our adaptive regulator can guarantee the convergence of states to zero or at least uniform ultimate boundedness of all signals in the closed loop when a not-necessarily-known modeling error is applied. The existence and boundedness of the control signal is always assured by employing a method of parameter “Hopping” and “Modified Hopping”, which appears in the weight updating laws. Simulations illustrate the potency of the method showing that by following the proposed procedure one can obtain asymptotic regulation despite the presence of modeling errors. Comparisons are also made to simple recurrent high order neural network (RHONN) controllers, showing that our approach is superior to the case of simple RHONN’s.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 1; 59-79
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A class of neuro-computational methods for assamese fricative classification
    Autorzy:
    Patgiri, C.
    Sarma, M.
    Sarma, K. K.
    Powiązania:
    https://bibliotekanauki.pl/articles/91763.pdf
    Data publikacji:
    2015
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    neuro-computational classifier
    fricative phonemes
    Assamese language
    Recurrent Neural Network
    RNN
    neuro fuzzy classifier
    linear prediction cepstral coefficients
    LPCC
    self-organizing map
    SOM
    adaptive neuro-fuzzy inference system
    ANFIS
    klasyfikator neuronowy
    klasyfikator neuronowo rozmyty
    sieć Kohonena
    Opis:
    In this work, a class of neuro-computational classifiers are used for classification of fricative phonemes of Assamese language. Initially, a Recurrent Neural Network (RNN) based classifier is used for classification. Later, another neuro fuzzy classifier is used for classification. We have used two different feature sets for the work, one using the specific acoustic-phonetic characteristics and another temporal attributes using linear prediction cepstral coefficients (LPCC) and a Self Organizing Map (SOM). Here, we present the experimental details and performance difference obtained by replacing the RNN based classifier with an adaptive neuro fuzzy inference system (ANFIS) based block for both the feature sets to recognize Assamese fricative sounds.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2015, 5, 1; 59-70
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Intelligent Retail Forecasting System for New Clothing Products Considering Stock-out
    Inteligentny system przewidywania sprzedaży detalicznej nowych produktów odzieżowych uwzględniający wyprzedaż
    Autorzy:
    Huang, H.
    Liu, Q.
    Powiązania:
    https://bibliotekanauki.pl/articles/232823.pdf
    Data publikacji:
    2017
    Wydawca:
    Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych
    Tematy:
    intelligent forecasting system
    demand estimation
    stock out
    adaptive neuro fuzzy inference system
    new clothing product
    inteligentny system prognozowania
    prognozowanie popytu
    system adaptacyjno neuronowy
    dane rozproszone
    selekcja danych
    Opis:
    Improving the accuracy of forecasting is crucial but complex in the clothing industry, especially for new products, with the lack of historical data and a wide range of factors affecting demand. Previous studies more concentrate on sales forecasting rather than demand forecasting, and the variables affecting demand remained to be optimized. In this study, a two-stage intelligent retail forecasting system is designed for new clothing products. In the first stage, demand is estimated with original sales data considering stock-out. The adaptive neuro fuzzy inference system (ANFIS) is introduced into the second stage to forecast demand. Meanwhile a data selection process is presented due to the limited data of new products. The empirical data are from a Canadian fast-fashion company. The results reveal the relationship between demand and sales, demonstrate the necessity of integrating the demand estimation process into a forecasting system, and show that the ANFIS-based forecasting system outperforms the traditional ANN technique.
    Poprawa dokładności prognozowania jest bardzo istotna, ale skomplikowana w przypadku przemysłu odzieżowego, zwłaszcza dla nowych produktów oraz szerokiego zakresu czynników wpływających na popyt. Wcześniejsze badania bardziej koncentrowały się na prognozowaniu sprzedaży, niż prognozowaniu popytu. Zmienne wpływające na popyt powinny zostać zoptymalizowane. W tym badaniu opracowano dwustopniowy inteligentny system prognozowania sprzedaży detalicznej przeznaczony dla nowych produktów odzieżowych. W pierwszym etapie, popyt jest określony za pośrednictwem oryginalnych danych dotyczących sprzedaży. Adaptacyjny neuronowy system danych rozproszonych (ANFIS) jest wprowadzony w drugim etapie do prognozowania popytu. Jednocześnie prezentowany jest proces selekcji danych. Dane empiryczne pochodzą z kanadyjskiej firmy.
    Źródło:
    Fibres & Textiles in Eastern Europe; 2017, 1 (121); 10-16
    1230-3666
    2300-7354
    Pojawia się w:
    Fibres & Textiles in Eastern Europe
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction of backbreak in open pit blasting by Adaptive Neuro-Fuzzy Inference System
    Prognozowanie spękań skał przy pracach strzałowych w kopalniach odkrywkowych przy użyciu metod neuronowych i wnioskowania rozmytego (ANFIS) zastosowanych w modelu adaptywnym
    Autorzy:
    Bazzazi, A. A.
    Esmaeili, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/219044.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    prace strzałowe
    pękanie skał
    system wnioskowania wykorzystujący elementy sieci neuronowych i logiki rozmytej
    kopalnia rud żelaza Sangan
    blasting
    backbreak
    adaptive neuro-fuzzy inference system
    Sangan iron mine
    Opis:
    Adaptive neuro-fuzzy inference system (ANFIS) is powerful model in solving complex problems. Since ANFIS has the potential of solving nonlinear problem and can easily achieve the input-output mapping, it is perfect to be used for solving the predicting problem. Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. In this paper, ANFIS was applied to predict backbreak in Sangan iron mine of Iran. The performance of the model was assessed through the root mean squared error (RMSE), the variance account for (VAF) and the correlation coefficient (R2) computed from the measured of backbreak and model-predicted values of the dependent variables. The RMSE, VAF, R2 indices were calculated 0.6, 0.94 and 0.95 for ANFIS model. As results, these indices revealed that the ANFIS model has very good prediction performance.
    Adaptywny system wnioskowania wykorzystujący elementy sieci neuronowych i logiki rozmytej (ANFIS) stanowi potężny narzędzie do rozwiązywania złożonych problemów. Ponieważ model ANFIS może być wykorzystywany do rozwiązywania problemów nieliniowych i umożliwia wygodne przedstawienie problemu w formie: wejście - wyjście, jest idealnym narzędziem do rozwiązywania problemów związanych z prognozowaniem. Pękanie skał w odkrywce jest jednym z niekorzystnych skutków prowadzenia prac strzałowych, powoduje niestabilność ścian, uszkodzenia maszyn i urządzeń, nieodpowiednią fragmentację skał oraz prowadzi do obniżenia efektywności wierceń. W pracy przedstawiono zastosowanie systemu ANFIS do prognozowania pękań skał w kopalni rud żelaza w Sangan (Iran). Działanie modelu zbadano na podstawie wartości błędu średniokwadratowego (RMSE), wariancji (VAF) i współczynnika korelacji (R2) obliczonego na podstawie pomiarów pęknięć skał i wartości uzyskanych z modelowania. Wartości wskaźników RMSE, VAF i R2 obliczonych przy użyciu modelu ANFIS wynoszą odpowiednio 0.6, 0.94 i 0.95. Wielkości te wyraźnie potwierdzają wysoką skuteczność modelu.
    Źródło:
    Archives of Mining Sciences; 2012, 57, 4; 933-943
    0860-7001
    Pojawia się w:
    Archives of Mining Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Estimation of discharge correction factor of modified Parshall flume using ANFIS and ANN
    Autorzy:
    Saran, D.
    Tiwari, N. K.
    Powiązania:
    https://bibliotekanauki.pl/articles/1818494.pdf
    Data publikacji:
    2020
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    Discharge Correction Factor
    Adaptive Neuro-Fuzzy Inference System
    artificial neural network
    Multiple Non-linear Regression
    parshall flumes
    współczynnik korygujący wyładowania
    adaptacyjny system neuronowo-rozmyty
    sztuczna sieć neuronowa
    regresja wielokrotna nieliniowa
    Opis:
    Purpose: To evaluate and compare the capability of ANFIS (Adaptive Neuro-Fuzzy-Inference System), ANN (Artificial Neural Network), and MNLR (Multiple Non-linear Regression) techniques in the estimation and formulation of Discharge Correction Factor (Cd) of modified Parshall flumes as based on linear relations and errors between input and output data. Design/methodology/approach: Acknowledging the necessity of further research in this field, experiments were conducted in the Hydraulics Laboratory of Civil Engineering Department, National Institute of Technology, Kurukshetra, India. The Parshall flume characteristics, associated longitudinal slopes and the discharge passing through the flume were varied. Consequent water depths at specific points in Parshall flumes were noted and the values of Cd were computed. In this manner, a data set of 128 observations was acquired. This was bifurcated arbitrarily into a training dataset consisting of 88 observations and a testing dataset consisting of 40 observations. Models developed using the training dataset were checked on the testing dataset for comparison of the performance of each predictive model. Further, an empirical relationship was formulated establishing Cd as a function of flume characteristics, longitudinal slope, and water depth at specific points using the MNLR technique. Moreover, Cd was estimated using soft computing tools; ANFIS and ANN. Finally, a sensitivity analysis was done to find out the flume variable having the greatest influence on the estimation of Cd. Findings: The predictive accuracy of the ANN-based model was found to be better than the model developed using ANFIS, followed by the model developed using the MNLR technique. Further, sensitivity analysis results indicated that primary depth reading (Ha) as input parameter has the greatest influence on the prediction capability of the developed model. Research limitations/implications: Since the soft computing models are data based learning, hence the prediction capability of these models may dwindle if data is selected beyond the current data range, which is based on the experiments conducted under specific conditions. Further, since the present study has faced time and facility constraints, hence there is still a huge scope of research in this field. Different lateral slopes, combined lateral- longitudinal slopes, and more modified Parshall flume models of larger sizes can be added to increase the versatility of the current research. Practical implications: Cd of modified Parshall flumes can be predicted using the ANN- based prediction model more accurately as compared to other considered techniques. Originality/value: The comparative analysis of prediction models, as well as the formulation of relation, has been conducted in this study. In all the previous works, little to no soft computing techniques have been applied for the analysis of Parshall flumes. Even the regression techniques have been applied only on Parshall flumes of standard sizes. However, this paper includes not only Parshall flume of standard size but also a modified Parshall flume in its pursuit of predicting Cd with the help of ANN and ANFIS based prediction models along with MNLR technique.
    Źródło:
    Archives of Materials Science and Engineering; 2020, 105, 1; 17--30
    1897-2764
    Pojawia się w:
    Archives of Materials Science and Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Neuro-Fuzzy System Based on Logical Interpretation of If-then Rules
    Autorzy:
    Łęski, J.
    Henzel, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/911145.pdf
    Data publikacji:
    2000
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    system rozmyty
    implikacja rozmyta
    fuzzy implications
    approximate reasoning
    neuro-fuzzy systems
    soft computing
    Opis:
    Several important fuzzy implications and their properties are described on the basis of an axiomatic approach to the definition of the fuzzy implications. Then the idea of approximate reasoning using the generalized modus ponens and fuzzy implications is considered. The elimination of the non-informative part of the final fuzzy set before defuzzification plays the key role in this paper. After reviewing well-known fuzzy systems, a new artificial neural network based on logical interpretation of if-then rules (ANBLIR) is introduced. Moreover, this system automatically generates rules from numerical data. Applications of ANBLIR to pattern recognition on numerical examples using benchmark databases are indicated.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2000, 10, 4; 703-722
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Implication-Based Neuro-Fuzzy Architectures
    Autorzy:
    Rutkowska, D.
    Nowicki, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/911144.pdf
    Data publikacji:
    2000
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    system rozmyty
    implikacja rozmyta
    wnioskowanie rozmyte
    neuro-fuzzy systems
    fuzzy implications
    fuzzy inference
    Mamdani approach
    logical approach
    connectionist architectures
    Opis:
    This paper presents connectionist multi-layer architectures of neuro-fuzzy systems based on various fuzzy implications. The well-known Mamdani approach (constructive) and the logical approach (destructive) are considered. Two kinds of architectures, a simpler and a more general one, are distinguished. Examples of application to classification and control problems are provided.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2000, 10, 4; 675-701
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neuro-fuzzy modelling based on a deterministic annealing approach
    Autorzy:
    Czabański, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/908442.pdf
    Data publikacji:
    2005
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    system rozmyty
    sieć neuronowa
    ekstrakcja reguł
    fuzzy systems
    neural networks
    neuro-fuzzy systems
    rules extraction
    deterministic annealing
    prediction
    Opis:
    This paper introduces a new learning algorithm for artificial neural networks, based on a fuzzy inference system ANBLIR. It is a computationally effective neuro-fuzzy system with parametrized fuzzy sets in the consequent parts of fuzzy if-then rules, which uses a conjunctive as well as a logical interpretation of those rules. In the original approach, the estimation of unknown system parameters was made by means of a combination of both gradient and least-squares methods. The novelty of the learning algorithm consists in the application of a deterministic annealing optimization method. It leads to an improvement in the neuro-fuzzy modelling performance. To show the validity of the introduced method, two examples of application concerning chaotic time series prediction and system identification problems are provided.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2005, 15, 4; 561-576
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Extraction of fuzzy rules using deterministic annealing integrated with ε-insensitive learning
    Autorzy:
    Czabański, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/908395.pdf
    Data publikacji:
    2006
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    system rozmyty
    sieć neuronowa
    sieć neuronowa rozmyta
    ekstrakcja reguł
    fuzzy systems
    neural networks
    neuro-fuzzy systems
    rules extraction
    deterministic annealing
    Opis:
    A new method of parameter estimation for an artificial neural network inference system based on a logical interpretation of fuzzy if-then rules (ANBLIR) is presented. The novelty of the learning algorithm consists in the application of a deterministic annealing method integrated with ε-insensitive learning. In order to decrease the computational burden of the learning procedure, a deterministic annealing method with a “freezing” phase and ε-insensitive learning by solving a system of linear inequalities are applied. This method yields an improved neuro-fuzzy modeling quality in the sense of an increase in the generalization ability and robustness to outliers. To show the advantages of the proposed algorithm, two examples of its application concerning benchmark problems of identification and prediction are considered.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2006, 16, 3; 357-372
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neuro-rough-fuzzy approach for regression modelling from missing data
    Autorzy:
    Simiński, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/331298.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    system neuronowo-rozmyty
    ANNBFIS
    brakujące wartości
    zbiór przybliżony
    zbiór rozmyty
    neuro-fuzzy
    missing values
    marginalisation
    imputation
    rough fuzzy set
    clustering
    Opis:
    Real life data sets often suffer from missing data. The neuro-rough-fuzzy systems proposed hitherto often cannot handle such situations. The paper presents a neuro-fuzzy system for data sets with missing values. The proposed solution is a complete neuro-fuzzy system. The system creates a rough fuzzy model from presented data (both full and with missing values) and is able to elaborate the answer for full and missing data examples. The paper also describes the dedicated clustering algorithm. The paper is accompanied by results of numerical experiments.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 2; 461-476
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Artificial Intelligence Approaches to Fault Diagnosis for Dynamic Systems
    Autorzy:
    Patton, R. J.
    Lopez-Toribio, C. J.
    Uppal, F. J.
    Powiązania:
    https://bibliotekanauki.pl/articles/908290.pdf
    Data publikacji:
    1999
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    metoda sztucznej inteligencji
    rozpoznanie błędu
    modelowanie rozmyte
    system rozmyty
    artificial intelligence methods
    fault diagnosis
    residual generation
    fuzzy modelling
    neuro-fuzzy systems
    Opis:
    Recent approaches to fault detection and isolation (FDI) for dynamic systems using methods of integrating quantitative and qualitative model information, based upon artificial intelligence (AI) techniques are surveyed. In this study, the use of AI methods is considered an important extension to the quantitative model-based approach for residual generation in FDI. When quantitative models are not readily available, a correctly trained artificial neural network (ANN) can be used as a non-linear dynamic model of the system. However, the neural network does not easily provide insight into model behaviour; the model is explicit rather than implicit in form. This main difficulty can be overcome using qualitative modelling or rule-based inference methods. For example, fuzzy logic can be used together with state-space models or neural networks to enhance FDI diagnostic reasoning capabilities. The paper discusses the properties of several methods of combining quantitative and qualitative system information and their practical value for fault diagnosis of real process systems.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 1999, 9, 3; 471-518
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A hybrid model for modelling the salinity of the Tafna River in Algeria
    Hybrydowy model służący modelowaniu zasolenia rzeki Tafna w Algierii
    Autorzy:
    Houari, Khemissi
    Hartani, Tarik
    Remini, Boualem
    Lefkir, Abdelouhab
    Abda, Leila
    Heddam, Salim
    Powiązania:
    https://bibliotekanauki.pl/articles/292367.pdf
    Data publikacji:
    2019
    Wydawca:
    Instytut Technologiczno-Przyrodniczy
    Tematy:
    Adaptive-Network-Based Fuzzy Inference System (ANFIS)
    hybrid model
    neuro-fuzzy
    salinity
    salt flow
    Tafna River
    model hybrydowy
    przepływ soli
    rzeka Tafna
    system neuronowo-rozmyty
    system wnioskowania rozmytego (ANFIS)
    zasolenie
    Opis:
    In this paper, the capacity of an Adaptive-Network-Based Fuzzy Inference System (ANFIS) for predicting salinity of the Tafna River is investigated. Time series data of daily liquid flow and saline concentrations from the gauging station of Pierre du Chat (160801) were used for training, validation and testing the hybrid model. Different methods were used to test the accuracy of our results, i.e. coefficient of determination (R2), Nash–Sutcliffe efficiency coefficient (E), root of the mean squared error (RSR) and graphic techniques. The model produced satisfactory results and showed a very good agreement between the predicted and observed data, with R2 equal (88% for training, 78.01% validation and 80.00% for testing), E equal (85.84% for training, 82.51% validation and 78.17% for testing), and RSR equal (2% for training, 10% validation and 49% for testing).
    W pracy badano zdolność systemu wnioskowania rozmytego opartego na adaptacyjnej sieci (ANFIS) do przewidywania zasolenia rzeki Tafna. Do trenowania, oceny i testowania modelu hybrydowego wykorzystano serie pomiarów dobowych przepływów płynu i stężeń soli ze stacji pomiarowej w Pierre du Chat (160801). Dokładność wyników testowano za pomocą: współczynnika determinacji (R2), współczynnika wydajności Nasha–Sutcliffe’a (E), pierwiastka średniego błędu kwadratowego (RSR) i technik graficznych. Model dał zadowalające wyniki i wykazywał dobrą zgodność między danymi obserwowanymi a przewidywanymi: R2 (88% w przypadku uczenia sieci, 78.01% walidacji i 80.00% testowania), E (85.84% w przypadku uczenia sieci, 82.51% walidacji i 78.17% testowania) i RSR (2% w przypadku uczenia sieci, 10% walidacji i 49% testowania).
    Źródło:
    Journal of Water and Land Development; 2019, 40; 127-135
    1429-7426
    2083-4535
    Pojawia się w:
    Journal of Water and Land Development
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-29 z 29

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