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Tytuł:
Approximation abilities of neuro-fuzzy systems
Autorzy:
Mrówczyńska, M.
Gil, J.
Powiązania:
https://bibliotekanauki.pl/articles/225380.pdf
Data publikacji:
2009
Wydawca:
Politechnika Warszawska. Wydział Geodezji i Kartografii
Tematy:
systemy neuro-rozmyte
neuro-fuzzy systems
Opis:
The paper presents the operation of neuro-fuzzy systems of an adaptive type as specific structures of mathematical models, intended for the approximation of multi-variable functions in the domain of real numbers. Neuro-fuzzy systems operate on the basin of a set of interences "if-then", generated with the use of algorithms for the use of algorithms for the self-organisationof data grouping and the estimation of relations beetween fuzzy experiment results. The article includes a description of models of neuro-fuzzy systems by Takaga, Sugeno, Kang (TSK), and Wang and Mendel (WM), which, when defined as continuous functions, enable the approximation of any multi-variable non-linear functions. Moreover, the module structure of the models enables the implemantation of a parallel multi-layer structure, analogous to the structure of classic neural networks.
Źródło:
Reports on Geodesy; 2009, z. 2/87; 283-290
0867-3179
Pojawia się w:
Reports on Geodesy
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Merging of fuzzy models for neuro-fuzzy systems
Scalanie modeli rozmytych w systemach neuronowo-rozmytych
Autorzy:
Simiński, K.
Powiązania:
https://bibliotekanauki.pl/articles/375698.pdf
Data publikacji:
2011
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
neuro-fuzzy
fuzzy set
rule merging
similarity
ANNBFIS
Opis:
The merging of fuzzy model is widely used for reduction of rule number in fuzzy model. The supernumerosity of rules is mainly caused by grid partition of input domain. In the paper different cause for model merging is described. It is the need for creation of fuzzy model for large data set. In our solution the models are build basing data subset and then the submodels are merged into one. This approach enables quicker elaboration of submodels with relatively good knowledge generalisation ability without waiting for the whole data set to be processed. With passing time, the subsequent submodels are created and merged to create the better model.
Artykuł opisuje scalanie modeli rozmytych w systemach neuronowo-rozmytych wykorzystywane przy tworzeniu modeli dla dużych zbiorów danych. Nieraz zbiory danych są tak duże, że nie jest możliwe wypracowanie modelu od razu dla całego zbioru. Tworzy się zatem modele dla podzbiorów zbioru danych. Uzyskane w ten sposób modele są następnie scalane, by wypracować jeden model. Podejście to jest także korzystne, gdy wszystkie dane nie są dostępne, ale są dostarczane partiami. Wtedy wstępny model jest wypracowany zanim wszystkie dane zostaną dostarczone do systemu. Artykuł przedstawia sposób wyznaczania podobieństwa reguł w modelu rozmytym oraz opisuje system neuronowo-rozmyty budujący i scalający modele wypracowane dla podzbiorów.
Źródło:
Theoretical and Applied Informatics; 2011, 23, 2; 107-126
1896-5334
Pojawia się w:
Theoretical and Applied Informatics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Modified neuro-fuzzy TSK network and its application in electronic nose
Autorzy:
Osowski, S.
Brudzewski, K.
Tran-Hoai, L.
Powiązania:
https://bibliotekanauki.pl/articles/201226.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
neuro-fuzzy TSK networks
fuzzy clusterization
regression
classification
Opis:
The paper develops the modified structure of the Takagi-Sugeno-Kang neuro-fuzzy network with a theoretical basis for its adaptation. The simplified structure follows from the basic theoretical considerations concerning the way of creating the inference rules. The important point of this solution is the application of the fuzzy clustering algorithm to the input data. The efficiency of the proposed solution has been checked on the examples of regression and classification problems concerning the electronic nose.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2013, 61, 3; 675-680
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neuro-fuzzy modelling of blending process in cement plant
Autorzy:
Araromi, D O
Odewale, S A
Hamed, J O
Powiązania:
https://bibliotekanauki.pl/articles/102547.pdf
Data publikacji:
2015
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
neuro-fuzzy
blending
carbonate content
raw mix
Opis:
The profitability of a cement plant depends largely on the efficient operation of the blending stage, therefore, there is a need to control the process at the blending stage in order to maintain the chemical composition of the raw mix near or at the desired value with minimum variance despite variation in the raw material composition. In this work, neuro-fuzzy model is developed for a dynamic behaviour of the system to predict the total carbonate content in the raw mix at different clay feed rates. The data used for parameter estimation and model validation was obtained from one of the cement plants in Nigeria. The data was pre-processed to remove outliers and filtered using smoothening technique in order to reveal its dynamic nature. Autoregressive exogenous (ARX) model was developed for comparison purpose. ARX model gave high root mean square error (RMSE) of 5.408 and 4.0199 for training and validation respectively. Poor fit resulting from ARX model is an indication of nonlinear nature of the process. However, both visual and statistical analyses on neuro-fuzzy (ANFIS) model gave a far better result. RMSE of training and validation are 0.28167 and 0.7436 respectively, and the sum of square error (SSE) and R-square are 39.6692 and 0.9969 respectively. All these are an indication of good performance of ANFIS model. This model can be used for control design of the process.
Źródło:
Advances in Science and Technology. Research Journal; 2015, 9, 28; 27-33
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Flatness-based adaptive fuzzy control of spark-ignited engines
Autorzy:
Rigatos, G.G.
Siano, P.
Powiązania:
https://bibliotekanauki.pl/articles/91727.pdf
Data publikacji:
2014
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
adaptive fuzzy controller
spark-ignited engines
SI engines
performance criterion
neuro-fuzzy networks
neuro-fuzzy approximator
Lyapunov stability analysis
simulation experiment
Opis:
An adaptive fuzzy controller is designed for spark-ignited (SI) engines, under the constraint that the system’s model is unknown. The control algorithm aims at satisfying the H∞ tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the SI-engine model into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system’s parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H∞ tracking performance. The efficiency of the proposed adaptive fuzzy control scheme is checked through simulation experiments.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 4; 231-242
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Pole placement approach to discrete and neuro-fuzzy crane control system prototyping
Autorzy:
Smoczek, J.
Szpytko, J.
Powiązania:
https://bibliotekanauki.pl/articles/245378.pdf
Data publikacji:
2009
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
pole placement method
overhead travelling crane
neuro-fuzzy
Opis:
Today are observed rising requirements regarding increase productivity, reduced labour and maintenance cost, as well as optimizing the effectiveness of the material handling. The overhead travelling cranes play important role in selected manufacture applications. The paper presents methods of crane dynamic modelling and anti-sway discrete crane control system determining with using pole placement method (PPM). The TSK neuro-fuzzy crane controller was shown in the paper, as well as method of adaptation its control parameters to various values of rope length and masses of the load variables. The results of experiments carried out on real object were presented as well. Presented in the paper methods of crane dynamic modelling and control algorithm determining allow to prototype the effective anti-sway crane control systems. The method of determining conventional anti-sway crane control system based on discrete controllers type of PD elaborated with using pole placement method (PPM) was described in the paper. The TSK neuro-fuzzy crane controller was shown in the paper as well as method of adaptation its control parameters to various values ofrope length l and masses of the load m variables. The results of experiments carried out with using adaptive neuro-fuzzy TSK controller shown robustness on changeability of these variables and effectiveness of proposed control system.
Źródło:
Journal of KONES; 2009, 16, 4; 435-445
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Organization of security control of wireless telecommunication network based on fuzzy networks
Autorzy:
Aleksander, M. B.
Karpinskyi, V.
Khlaponin, Y.
Yudin, O.
Powiązania:
https://bibliotekanauki.pl/articles/114432.pdf
Data publikacji:
2016
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
linguistic variable
neuro-fuzzy network
neural network
wireless network
Opis:
The problem solution of security control of wireless computer network nodes, which is based on the use of the apparatus of fuzzy sets is presented in this article. The approach used in the paper aims to an automation that will improve the efficiency of control of the nodes and operations of a network administrator. The approach allows forming a system of security control of the wireless computer network based on neuro-fuzzy (hybrid) network, which is characterized by high adaptability, ease use, the ability to identify better the sequence of the analysis of vulnerabilities in the wireless computer network nodes. The feature of the proposed approach takes the dynamic nature of the wireless computer network into account.
Źródło:
Measurement Automation Monitoring; 2016, 62, 10; 341-344
2450-2855
Pojawia się w:
Measurement Automation Monitoring
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analysis of new method of initialisation of neuro - fuzzy systems with support vector machines
Analiza nowej metody inicjalizacji systemów neuronowo – rozmytych z wykorzystaniem maszyn wektorów wspierających
Autorzy:
Simiński, K.
Powiązania:
https://bibliotekanauki.pl/articles/375675.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
support vector machine (SVM)
neuro-fuzzy systems
classification
regression
Opis:
The correspondence between support vector machines and neuro-fuzzy systems is very interesting. The full equivalence for classification and partial for regression has been formally shown. The equivalence has very interesting implication. It is a base for a new method of initialization of neurofuzzy systems, ie. for creating of fuzzy rule base. The commonly used methods are based on reversion of item: the premises of fuzzy rules split input domain into region, thus premises of fuzzy rules can be elaborated by partition of input domain. This leads to three main classes of partition of input domain. The above mentioned equivalence results in new way of creating the rule base. Now the input domain is not partitioned, but the premises of fuzzy rules are extracted from support vector. The objective of the paper is to examine the advantages and disadvantages of this new method for creation of fuzzy rule bases for neuro-fuzzy systems.
Związek pomiedzy maszynami wektorów podpierajacych i systemami neuronoworozmytymi jest bardzo interesujący. Została wykazana pełna odpowiedniość między tymi systemami dla klasyfikacji i częściowa dla regresji. Odpowiedność ta ma bardzo ważną konsekwencję. Jest podstawa do opracowania nowego sposobu tworzenia bazy reguł dla systemu neuronowo-rozmytego. Dotychczasowe metody opieraja się na podziale przestrzeni wejściowej, a następnie przekształcenia tak powstałych regionów w przesłanki rozmytych reguł. Tutaj możliwe jest przekształcanie wektorów wspierających na przesłanki reguł rozmytych. Celem artykułu jest przebadanie możliwości stosowania takiego podejścia do inicjalizacji systemów neuronowo-rozmytych. Eksperymenty wykazują dosć istotną wadę tego podejścia. W jego wyniku powstają bardzo liczne zbiory reguł rozmytych, co zupełnie przeczy idei interpretowalności wiedzy w systemach neuronowo-rozmytych. Manipulacja pewnymi parametrami umożliwia zmiejszenie liczby reguł, jednak manipulacja ta jest trudna i wymaga wielu prób. Drugą dość istotna wadą jest wyraźnie wyższy błąd wypracowywany przez systemy inicjalizowane przez SVM w porównaniu do systemów, których bazy reguł tworzone sa˛ poprzez podział przestrzeni wejściowej.
Źródło:
Theoretical and Applied Informatics; 2012, 24, 3; 243-254
1896-5334
Pojawia się w:
Theoretical and Applied Informatics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analog Circuit Based on Computational Intelligence Techniques
Autorzy:
Oltean, G.
Hintea, S.
Şipos, E.
Powiązania:
https://bibliotekanauki.pl/articles/385049.pdf
Data publikacji:
2009
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
analog circuit design
optimization
genetic algorithm
neuro-fuzzy systems
Opis:
This paper presents a new method for analog circuit design optimization. Our approach turns to good account the advantages offered by computational intelligence techniques. Design objectives can be expressed in a flexible manner using fuzzy sets. This way appears the possibility to consider different degrees for requirement achievements and acceptability degree for a particular solution. Neuro-fuzzy systems (universal approximators) are used to model the complex multi-variable and nonlinear circuit performances. These models satisfy two main requirements: high accuracy and low computation complexity. An efficient and robust genetic algorithm does avoiding local minima the exploration of the large, multidimensional solution space in quest for the optimal solution.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2009, 3, 2; 63-69
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of competitive and transition petri layers in adaptive neuro-fuzzy controller
Autorzy:
Derugo, P.
Powiązania:
https://bibliotekanauki.pl/articles/1193155.pdf
Data publikacji:
2016
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
Petri layer
neuro-fuzzy
MRAS
competitive layer
transition layer
Opis:
The article is a summary of previous work on the possibility of using Petri layers in adaptive neuro-fuzzy controllers. In the first part of the paper the controller and two types of Petri layer have been presented, competitive layer which resets certain signals and transition layer which causes omission of signals. Layer properties were described and comparison has been made. In the second part of the paper, the results of a simulation showing the advantages and disadvantages of proposed solutions have been presented. Both quality of reference signal tracking and energetic cost of control process have been calculated. In the last part, analysis and comments on the results were made. Main conclusions are that transition Petri layer can significantly reduce growth of numerical cost of the algorithm despite the increase of fuzzy rules count. Also both competitive Petri layer and transition Petri layer by changing some inner signals can affect output value of the fuzzy system and thus the control quality indicators change. Most positive solutions have been pointed out
Źródło:
Power Electronics and Drives; 2016, 1, 36/1; 103-115
2451-0262
2543-4292
Pojawia się w:
Power Electronics and Drives
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analysis of adaptive neuro-fuzzy PD controller with competitive Petri layers in speed control system for DC motor
Autorzy:
Derugo, P.
Szabat, K.
Powiązania:
https://bibliotekanauki.pl/articles/97666.pdf
Data publikacji:
2013
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
adaptive neuro-fuzzy controller
Peri Layers
competitive layers
MRAS
Opis:
In the paper the issues related to the application of adaptive neuro-fuzzy controller for speed controller of an electrical motor are considered. Adaptive control structure with reference model (MRAS) is used. The standard controller is modified by the implementation of competitive Petri layers into its internal structure. The proposed modification improves the properties of the drive compared to the control structure with standard neuro-fuzzy controller. Theoretical considerations are confirmed by simulation studies experimental tests done on the laboratory stand.
Źródło:
Computer Applications in Electrical Engineering; 2013, 11; 267-280
1508-4248
Pojawia się w:
Computer Applications in Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
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:
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 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:
Daily Suspended Sediment Prediction Using Seasonal Time Series and Artificial Intelligence Techniques
Autorzy:
Üneş, Fatih
Taşar, Bestami
Demirci, Mustafa
Zelenakova, Martina
Kaya, Yunus Ziya
Varçin, Hakan
Powiązania:
https://bibliotekanauki.pl/articles/2069941.pdf
Data publikacji:
2021
Wydawca:
Politechnika Koszalińska. Wydawnictwo Uczelniane
Tematy:
prediction
neuro-fuzzy
sediment rating curve
support vector machine
suspended sediment
Opis:
Estimating the amount of suspended sediment in rivers correctly is important due to the adverse impacts encountered during the design and maintenance of hydraulic structures such as dams, regulators, water channels and bridges. The sediment concentration and discharge currents have usually complex relationship, especially on long term scales, which can lead to high uncertainties in load estimates for certain components. In this paper, with several data-driven methods, including two types of perceptron support vector machines with radial basis function kernel (SVM-RBF), and poly kernel learning algorithms (SVM-PK), Library SVM (LibSVM), adaptive neuro-fuzzy (NF) and statistical approaches such as sediment rating curves (SRC), multi linear regression (MLR) are used for forecasting daily suspended sediment concentration from daily temperature of water and streamflow in the river. Daily data are measured at Augusta station by the US Geological Survey. 15 different input combinations (1 to 15) were used for SVM-PK, SVM-RBF, LibSVM, NF and MLR model studies. All approaches are compared to each other according to three statistical criteria; mean absolute errors (MAE), root mean square errors (RMSE) and correlation coefficient (R). Of the applied linear and nonlinear methods, LibSVM and NF have good results, but LibSVM generates a slightly better fit under whole daily sediment values.
Źródło:
Rocznik Ochrona Środowiska; 2021, 23; 117--137
1506-218X
Pojawia się w:
Rocznik Ochrona Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
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