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Tytuł:
2D Cadastral Coordinate Transformation using extreme learning machine technique
Autorzy:
Ziggah, Y. Y.
Issaka, Y.
Laari, P. B.
Hui, Z.
Powiązania:
https://bibliotekanauki.pl/articles/145372.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
transformacja współrzędnych
sieci neuronowe
dane geodezyjne
sieć radialna
coordinate transformation
extreme learning machine
backpropagation neural network
radial basis function neural network
geodetic datum
Opis:
Land surveyors, photogrammetrists, remote sensing engineers and professionals in the Earth sciences are often faced with the task of transferring coordinates from one geodetic datum into another to serve their desired purpose. The essence is to create compatibility between data related to different geodetic reference frames for geospatial applications. Strictly speaking, conventional techniques of conformal, affine and projective transformation models are mostly used to accomplish such task. With developing countries like Ghana where there is no immediate plans to establish geocentric datum and still rely on the astro-geodetic datums as it national mapping reference surface, there is the urgent need to explore the suitability of other transformation methods. In this study, an effort has been made to explore the proficiency of the Extreme Learning Machine (ELM) as a novel alternative coordinate transformation method. The proposed ELM approach was applied to data found in the Ghana geodetic reference network. The ELM transformation result has been analysed and compared with benchmark methods of backpropagation neural network (BPNN), radial basis function neural network (RBFNN), two-dimensional (2D) affine and 2D conformal. The overall study results indicate that the ELM can produce comparable transformation results to the widely used BPNN and RBFNN, but better than the 2D affine and 2D conformal. The results produced by ELM has demonstrated it as a promising tool for coordinate transformation in Ghana.
Źródło:
Geodesy and Cartography; 2018, 67, 2; 321-343
2080-6736
2300-2581
Pojawia się w:
Geodesy and Cartography
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A fast neural network learning algorithm with approximate singular value decomposition
Autorzy:
Jankowski, Norbert
Linowiecki, Rafał
Powiązania:
https://bibliotekanauki.pl/articles/330870.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
Moore–Penrose pseudoinverse
radial basis function network
extreme learning machine
kernel method
machine learning
singular value decomposition
deep extreme learning
principal component analysis
pseudoodwrotność Moore–Penrose
radialna funkcja bazowa
maszyna uczenia ekstremalnego
uczenie maszynowe
analiza składników głównych
Opis:
The learning of neural networks is becoming more and more important. Researchers have constructed dozens of learning algorithms, but it is still necessary to develop faster, more flexible, or more accurate learning algorithms. With fast learning we can examine more learning scenarios for a given problem, especially in the case of meta-learning. In this article we focus on the construction of a much faster learning algorithm and its modifications, especially for nonlinear versions of neural networks. The main idea of this algorithm lies in the usage of fast approximation of the Moore–Penrose pseudo-inverse matrix. The complexity of the original singular value decomposition algorithm is O(mn2). We consider algorithms with a complexity of O(mnl), where l < n and l is often significantly smaller than n. Such learning algorithms can be applied to the learning of radial basis function networks, extreme learning machines or deep ELMs, principal component analysis or even missing data imputation.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2019, 29, 3; 581-594
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
ANN-based failure modeling of classes of aircraft engine components using radial basis functions
Modelowanie uszkodzeń elementów silnika samolotowego w oparciu o sztuczne sieci neuronowe o radialnych funkcjach bazowych
Autorzy:
Al-Garni, Ahmed
Abdelrahman, Wael
Abdallah, Ayman
Powiązania:
https://bibliotekanauki.pl/articles/301913.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
neural network
radial basis function
Reliability
engine components
sieć neuronowa
radialna funkcja bazowa
niezawodność
elementy silnika
Opis:
The objective of this research is to present a model to predict failure of two categories of critical aircraft engine components; nonrotating components such as valves and gearboxes, and rotating components such as engine turbines. The work utilizes Weibull regression and artificial neural networks employing Back Propagation (BP) as well as Radial Basis Functions (RBF). The model utilizes training failure data collected from operators of turboprop aircraft working in harsh desert conditions, where sand erosion is a detrimental factor in reducing turbine life. Accordingly, the model is more suited for accurate prediction of life of critical components of such engines. The algorithm, which uses Radial Basis Function (RBF) NN, uses a closest point specifier. The activation is based on the deviation of the earlier prototype from the input vector. Two earlier models are used for comparison purposes; namely Weibull regression modeling and Feed-Forward BP network. Comparison results show that the failure times represented by RBF are in better compromise with actual failure data than both earlier modeling methods. Moreover, the technique has comparatively higher efficiency as the neuron’s number in each layer of ANN is reduced, to decrease computation time, with minimum effect on the accuracy of results.
Celem pracy jest przedstawienie modelu służącego do predykcji uszkodzeń dwóch kategorii krytycznych elementów silnika samolotowego: elementów nieobrotowych, takich jak zawory i skrzynie biegów oraz elementów obrotowych, takich jak turbiny silnika. W pracy wykorzystano regresję Weibulla i sztuczne sieci neuronowe oparte na propagacji wstecznej oraz radialnych funkcjach bazowych (RBF). Model wykorzystuje dane o błędach zebrane od operatorów samolotów turbośmigłowych pracujących w trudnych warunkach pustynnych, gdzie erozja powodowana przez piasek stanowi szkodliwy czynnik ograniczający żywotność turbin. Prezentowany model jest więc szczególnie przydatny do trafnego prognozowania żywotności krytycznych elementów takich silników. Algorytm, który wykorzystuje sieci neuronowe o radialnych funkcjach bazowych, używa specyfikatora najbliższego punktu. Aktywacja bazuje na odchyleniu wcześniejszego prototypu od wektora wejściowego. Dwa wcześniejsze modele oparte na regresji Weibulla (Weibull regression modeling) oraz sieciach typu Feed-Forward Backpropagation wykorzystano do badań porównawczych. Wyniki porównania pokazują, że czasy uszkodzeń odwzorowane przez RBF pozostają w większej zgodzie z rzeczywistymi danymi o uszkodzeniach niż w przypadku obu wcześniejszych metod modelowania. Co więcej, technika ta ma porównywalnie większą efektywność, ponieważ liczba neuronów w każdej warstwie sieci neuronowej została zredukowana tak aby zmniejszyć czas obliczeń, przy minimalnym wpływie na dokładność wyników.
Źródło:
Eksploatacja i Niezawodność; 2019, 21, 2; 311-317
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Applying LCS to affective image classification in spatial - frequency domain
Autorzy:
Lee, P. -M.
Hsiao, T.-C.
Powiązania:
https://bibliotekanauki.pl/articles/91808.pdf
Data publikacji:
2014
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
image classification
pattern recognition
Hilbert-Huang transform
HHT
empirical mode decomposition
EMD
Hilbert transform
HT
Extended Classifier Systems
XCSs
Area Under Curve
AUC
Radial-Basis Function Network
RBF Network
LCS
Opis:
Recent studies have utilizes color, texture, and composition information of images to achieve affective image classification. However, the features related to spatial-frequency domain that were proven to be useful for traditional pattern recognition have not been tested in this field yet. Furthermore, the experiments conducted by previous studies are not internationally-comparable due to the experimental paradigm adopted. In addition, contributed by recent advances in methodology, that are, Hilbert-Huang Transform (HHT) (i.e. Empirical Mode Decomposition (EMD) and Hilbert Transform (HT)), the resolution of frequency analysis has been improved. Hence, the goal of this research is to achieve the affective image-classification task by adopting a standard experimental paradigm introduces by psychologists in order to produce international-comparable and reproducible results; and also to explore the affective hidden patterns of images in the spatial-frequency domain. To accomplish these goals, multiple human-subject experiments were conducted in laboratory. Extended Classifier Systems (XCSs) was used for model building because the XCS has been applied to a wide range of classification tasks and proved to be competitive in pattern recognition. To exploit the information in the spatial-frequency domain, the traditional EMD has been extended to a two-dimensional version. To summarize, the model built by using the XCS achieves Area Under Curve (AUC) = 0.91 and accuracy rate over 86%. The result of the XCS was compared with other traditional machine-learning algorithms (e.g., Radial-Basis Function Network (RBF Network)) that are normally used for classification tasks. Contributed by proper selection of features for model building, user-independent findings were obtained. For example, it is found that the horizontal visual stimulations contribute more to the emotion elicitation than the vertical visual stimulation. The effect of hue, saturation, and brightness; is also presented.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 2; 99-123
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparison of MLP and RBF Neural Networks in the Task of Classifying the Diameters of Water Pipes
Autorzy:
Gvishiani, Zurab
Dawidowicz, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/2174907.pdf
Data publikacji:
2022
Wydawca:
Politechnika Koszalińska. Wydawnictwo Uczelniane
Tematy:
water distribution system
hydraulic calculation
selection of diameter
water pipe
artificial neural network
radial basis function
multilayer perceptron
Opis:
Hydraulic calculations of water distribution systems are currently performed using computer programs. In addition to the basic calculation procedure, modules responsible for evaluating the obtained calculation results are introduced more and more often into the programs. This article presents the results of research on artificial neural networks with a radial base function (RBF) and a multilayer perceptron (MLP), aimed at determining whether they can be used to model the relationship between the variables describing the computational section of the water distribution system and the diameter of the water pipe. The classification capabilities of the RBF and MLP networks were analyzed according to the number of neurons in the hidden layer of the network. A comparative analysis of RBF networks with multilayer perceptron (MLP) networks was performed. The results showed that the MLP networks have much better classification properties and are better suited for the task of assessing the selected diameters of the water pipes.
Źródło:
Rocznik Ochrona Środowiska; 2022, 24; 505--519
1506-218X
Pojawia się w:
Rocznik Ochrona Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparison of prototype selection algorithms used in construction of neural networks learned by SVD
Autorzy:
Jankowski, N.
Powiązania:
https://bibliotekanauki.pl/articles/330020.pdf
Data publikacji:
2018
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
radial basis function network
extreme learning machine
kernel method
prototype selection
machine learning
k nearest neighbours
radialna funkcja bazowa
metoda jądrowa
uczenie maszynowe
metoda k najbliższych sąsiadów
Opis:
Radial basis function networks (RBFNs) or extreme learning machines (ELMs) can be seen as linear combinations of kernel functions (hidden neurons). Kernels can be constructed in random processes like in ELMs, or the positions of kernels can be initialized by a random subset of training vectors, or kernels can be constructed in a (sub-)learning process (sometimes by k-means, for example). We found that kernels constructed using prototype selection algorithms provide very accurate and stable solutions. What is more, prototype selection algorithms automatically choose not only the placement of prototypes, but also their number. Thanks to this advantage, it is no longer necessary to estimate the number of kernels with time-consuming multiple train-test procedures. The best results of learning can be obtained by pseudo-inverse learning with a singular value decomposition (SVD) algorithm. The article presents a comparison of several prototype selection algorithms co-working with singular value decomposition-based learning. The presented comparison clearly shows that the combination of prototype selection and SVD learning of a neural network is significantly better than a random selection of kernels for the RBFN or the ELM, the support vector machine or the kNN. Moreover, the presented learning scheme requires no parameters except for the width of the Gaussian kernel.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2018, 28, 4; 719-733
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparison of two interpolation methods for empirical mode decomposition based evaluation of radiographic femur bone images
Autorzy:
Udhayakumar, G.
Sujatha, C. M.
Ramakrishnan, S.
Powiązania:
https://bibliotekanauki.pl/articles/306313.pdf
Data publikacji:
2013
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
anisotropy
bone mineral density
hierarchical b-spline
intrinsic mode function
radial basis function multiquadratic
trabecular soft bone
texture analysis
anizotropia
gęstość mineralna kości
analiza tekstury
Opis:
Analysis of bone strength in radiographic images is an important component of estimation of bone quality in diseases such as osteoporosis. Conventional radiographic femur bone images are used to analyze its architecture using bi-dimensional empirical mode decomposition method. Surface interpolation of local maxima and minima points of an image is a crucial part of bi-dimensional empirical mode decomposition method and the choice of appropriate interpolation depends on specific structure of the problem. In this work, two interpolation methods of bi-dimensional empirical mode decomposition are analyzed to characterize the trabecular femur bone architecture of radiographic images. The trabecular bone regions of normal and osteoporotic femur bone images (N = 40) recorded under standard condition are used for this study. The compressive and tensile strength regions of the images are delineated using pre-processing procedures. The delineated images are decomposed into their corresponding intrinsic mode functions using interpolation methods such as Radial basis function multiquadratic and hierarchical b-spline techniques. Results show that bi-dimensional empirical mode decomposition analyses using both interpolations are able to represent architectural variations of femur bone radiographic images. As the strength of the bone depends on architectural variation in addition to bone mass, this study seems to be clinically useful.
Źródło:
Acta of Bioengineering and Biomechanics; 2013, 15, 2; 73-80
1509-409X
2450-6303
Pojawia się w:
Acta of Bioengineering and Biomechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Face Recognition Using Canonical Correlation, Discrimination Power, and Fractional Multiple Exemplar Discriminant Analyses
Autorzy:
Hajiarbabi, M.
Agah, A.
Powiązania:
https://bibliotekanauki.pl/articles/384779.pdf
Data publikacji:
2015
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
face recognition
Canonical Correlation Analysis
Discrimination Power Analysis
Multiple Exemplar Discriminant Analysis
Radial Basis Function neural
networks
Opis:
Face recognition is a biometric identification method which compared to other methods, such as finger print identification, speech, signature, hand written and iris recognition is shown to be more noteworthy both theoretically and practically. Biometric identification methods have various applications such as in film processing, control access networks, among many. The automatic recognition of a human face has become an important problem in pattern recognition, due to (1) the structural similarity of human faces, and (2) great impact of factors such as illumination conditions, facial expression and face orientation. These have made face recognition one of the most challenging problems in pattern recognition. Appearance-based methods are one of the most common methods in face recognition, which can be categorized into linear and nonlinear methods. In this paper face recognition using Canonical Correlation Analysis is introduced, along with the review of the linear and nonlinear appearance-based methods. Canonical Correla- tion Analysis finds the linear combinations between two sets of variables which have maximum correlation with one another. Discriminant Power analysis and Fractional Multiple Discriminant Analysis has been used to extract features from the image. The results provided in this paper show the advantage of this method compared to other methods in this field.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2015, 9, 4; 18-27
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Free vibration of structures by radial basis function – pseudospectral method
Autorzy:
Krowiak, A.
Powiązania:
https://bibliotekanauki.pl/articles/128260.pdf
Data publikacji:
2014
Wydawca:
Politechnika Poznańska. Instytut Mechaniki Stosowanej
Tematy:
meshless methods
radial basis function
pseudospectral methods
metody bezsiatkowe
radialna funkcja bazowa
metoda pseudospektralna
Opis:
The paper deals with the use of the radial basis function-pseudospectral method in vibration analysis of twodimensional mechanical structures. The method combines meshless features of radial basis function (RBF) with efficiency and simplicity of the pseudospectral method. In present work the main emphasis is laid on appropriate assumption of the interpolant for the sought function due to the number of the boundary conditions in analysed problem. This interpolation function enables to obtain the weighting coefficients for derivative approximation in a governing equation. The method is applied to free vibration analysis of arbitrarily shaped membrane and plate.
Źródło:
Vibrations in Physical Systems; 2014, 26; 113-120
0860-6897
Pojawia się w:
Vibrations in Physical Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Integrated fault-tolerant control of a quadcopter UAV with incipient actuator faults
Autorzy:
Kantue, Paulin
Pedro, Jimoh O.
Powiązania:
https://bibliotekanauki.pl/articles/2172129.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
fault tolerant control
quadrocopter
incipient actuator fault
radial basis function
neural network
sterowanie tolerujące uszkodzenia
kwadrokopter
radialna funkcja bazowa
sieć neuronowa
Opis:
An integrated approach to the fault-tolerant control (FTC) of a quadcopter unmanned aerial vehicle (UAV) with incipient actuator faults is presented. The framework is comprised of a radial basis function neural network (RBFNN) fault detection and diagnosis (FDD) module and a reconfigurable flight controller (RFC) based on the extremum seeking control approach. The dynamics of a quadcopter subject to incipient actuator faults are estimated using a nonlinear identification method comprising a continuous forward algorithm (CFA) and a modified golden section search (GSS) one. A time-difference-of-arrival (TDOA) method and the post-fault system estimates are used within the FDD module to compute the fault location and fault magnitude. The impact of bi-directional uncertainty and FDD detection time on the overall FTC performance and system recovery is assessed by simulating a quadcopter UAV during a trajectory tracking mission and is found to be robust against incipient actuator faults during straight and level flight and tight turns.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2022, 32, 4; 601--617
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Lan interconnection unit based on an artificial neural network
Autorzy:
Jalab, Hamid A.
Powiązania:
https://bibliotekanauki.pl/articles/1955324.pdf
Data publikacji:
2006
Wydawca:
Politechnika Gdańska
Tematy:
LAN bridge
neural networks
radial basis function (RBF)
Opis:
This paper presents the design of an intelligent interconnection unit based on an artificial neural network (ANN), used when two local area networks (LAN) with different IEEE 802 standard protocols are connected. The proposed ANN is used to activate execution of suitable procedures bridging 802.X LAN and 802.Y LAN.
Źródło:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2006, 10, 3; 339-346
1428-6394
Pojawia się w:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Lokalizacja punktów pomiarowych w systemie do trójwymiarowego pozycjonowania ciała wybranymi metodami sztucznej inteligencji
Detection of measurement points in a 3D body positioning system by means of artificial intelligence
Autorzy:
Czechowicz, A.
Tokarczyk, R.
Powiązania:
https://bibliotekanauki.pl/articles/131086.pdf
Data publikacji:
2009
Wydawca:
Stowarzyszenie Geodetów Polskich
Tematy:
fotogrametria
pozycjonowanie ciała
sieci neuronowe
perceptron wielowarstwowy
wsteczna propagacja błędów
sieci z radialnymi funkcjami bazowymi
photogrammetry
body positioning
neural networks
multi-layer perceptron
error back-propagation
radial basis function networks
Opis:
Fotogrametryczny system cyfrowy do pomiaru ciała ludzkiego dla celów badania wad postawy służy do wyznaczania przestrzennego położenia wybranych jego punktów. Wymaga on pomierzenia na zdjęciach cyfrowych trzech grup punktów, zwanych w tytule referatu punktami pomiarowymi: fotopunktów, markerów sygnalizowanych na pacjencie oraz źrenic oczu. Fotopunkty to czarno-białe sygnały pozwalające na orientację w przestrzeni modelu utworzonego ze zdjęć. Markery to styropianowe kulki o średnicy 4÷5 mm sygnalizujące wybrane elementy kośćca umieszczone na powierzchni ciała. Artykuł dotyczy wykorzystania sieci neuronowych do lokalizacji fotopunktów i styropianowych markerów. Zadaniem sieci jest klasyfikacja kolejnych fragmentów obrazu na zawierające obraz fotopunktu, markera lub niezawierające obrazu żadnego z nich. W ramach badań sprawdzono możliwość przeprowadzenia zdefiniowanej powyżej klasyfikacji sieciami o architekturze wielowarstwowego perceptronu (ang. Multi Layer Perceptron –MLP) ze wsteczną propagacją błędu oraz sieciami z radialnymi funkcjami bazowymi RBF (ang. Radial Basis Function Networks). Zweryfikowano przydatność reprezentacji opartej na informacji o rozkładzie wartości gradientu oraz jego kierunku dla celów wykrycia punktów pomiarowych. Wspomniana reprezentacja wywodzi się z badań nad selekcją podobrazów dla potrzeb dopasowania zdjęć lotniczych.
A digital photogrammetric system for making measurements of the human body for the purpose of studying faulty posture is designed to determine the three-dimensional location of selected points in the human body. It requires the measurement of three groups of points on digital images, points referred to in this paper’s title as measurement points, i.e. control points, markers indicated on the patient’s body and pupils of the eyes. Control points are black and white signals permitting the correct orientation in space of a model created from the images. The markers are balls of polystyrene foam of 4-5 mm diameter, placed on the body, which indicate selected elements of the human skeleton. This paper describes the utilisation of neural networks to locate control points and markers. The aim of the networks is to classify consecutive fragments of an image as containing control points, containing markers or not containing any of these features. The research covered evaluation of the possibility of conducting this classification using Multi Layer Perceptron Networks with back propagation of errors as well as with Radial Basis Function Networks. The usefulness of a representation based on information about the distribution of gradient value and direction for the purpose of the detection of measurement points has been verified. This representation comes from earlier research on the selection of subimages for the purpose of matching the aerial pictures.
Źródło:
Archiwum Fotogrametrii, Kartografii i Teledetekcji; 2009, 20; 67-79
2083-2214
2391-9477
Pojawia się w:
Archiwum Fotogrametrii, Kartografii i Teledetekcji
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Meshless local radial point interpolation (MLRPI) for generalized telegraph and heat diffusion equation with non-local boundary conditions
Autorzy:
Shivanian, E.
Khodayari, A.
Powiązania:
https://bibliotekanauki.pl/articles/279501.pdf
Data publikacji:
2017
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
non-local boundary condition
meshless local radial point interpolation (MLRPI) method
local weak formulation
radial basis function
telegraph equation
Opis:
In this paper, the meshless local radial point interpolation (MLRPI) method is formulated to the generalized one-dimensional linear telegraph and heat diffusion equation with non-local boundary conditions. The MLRPI method is categorized under meshless methods in which any background integration cells are not required, so that all integrations are carried out locally over small quadrature domains of regular shapes, such as lines in one dimensions, circles or squares in two dimensions and spheres or cubes in three dimensions. A technique based on the radial point interpolation is adopted to construct shape functions, also called basis functions, using the radial basis functions. These shape functions have delta function property in the frame work of interpolation, therefore they convince us to impose boundary conditions directly. The time derivatives are approximated by the finite difference time- -stepping method. We also apply Simpson’s integration rule to treat the non-local boundary conditions. Convergency and stability of the MLRPI method are clarified by surveying some numerical experiments.
Źródło:
Journal of Theoretical and Applied Mechanics; 2017, 55, 2; 571-582
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural Network Model for Control of Operating Modes of Crushing and Grinding Complex
Autorzy:
Kalinchyk, Vasyl
Meita, Olexandr
Pobigaylo, Vitalii
Borychenko, Olena
Kalinchyk, Vitalii
Powiązania:
https://bibliotekanauki.pl/articles/2174915.pdf
Data publikacji:
2022
Wydawca:
Politechnika Koszalińska. Wydawnictwo Uczelniane
Tematy:
classification
modelling
neural network
radial basis function network
RBF
multilayer perceptron
MLP
Opis:
This article investigates the application of neural network models to create automated control systems for industrial processes. We reviewed and analysed works on dispatch control and evaluation of equipment operating modes and the use of artificial neural networks to solve problems of this type. It is shown that the main requirements for identification models are the accuracy of estimation and ease of algorithm implementation. It is shown that artificial neural networks meet the requirements for accuracy of classification problems, ease of execution and speed. We considered the structures of neural networks that can be used to recognise the modes of operation of technological equipment. Application of the model and structure of networks with radial basis functions and multilayer perceptrons for identifying the mode of operation of equipment under given conditions is substantiated. The input conditions for constructing neural network models of two types with a given three-layer structure are offered. The results of training neural models on the model of a multilayer perceptron and a network with radial basis functions are presented. The estimation and comparative analysis of models depending on model parameters are made. It is shown that networks with radial basis functions offer greater accuracy in solving identification problems. The structural scheme of the automated process control system with mode identification based on artificial neural networks is offered.
Źródło:
Rocznik Ochrona Środowiska; 2022, 24; 26--40
1506-218X
Pojawia się w:
Rocznik Ochrona Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neuro-adaptive cooperative control for high-order nonlinear multi-agent systems with uncertainties
Autorzy:
Peng, Cheng
Zhang, Anguo
Li, Junyu
Powiązania:
https://bibliotekanauki.pl/articles/2055174.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
multiagent system
radial basis function
RBF neural network
sliding mode control
cooperative control
system wieloagentowy
radialna funkcja bazowa
sieć neuronowa RBF
sterowanie ślizgowe
Opis:
The consensus problem for a class of high-order nonlinear multi-agent systems (MASs) with external disturbance and system uncertainty is studied. We design an online-update radial basis function (RBF) neural network based distributed adaptive control protocol, where the sliding model control method is also applied to eliminate the influence of the external disturbance and system uncertainty. System consensus is verified by using the Lyapunov stability theorem, and sufficient conditions for cooperative uniform ultimately boundedness (CUUB) are also derived. Two simulation examples demonstrate the effectiveness of the proposed method for both homogeneous and heterogeneous MASs.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2021, 31, 4; 635--645
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 24

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