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Wyszukujesz frazę "fuzzy-neural networks" wg kryterium: Temat

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    Wyświetlanie 1-8 z 8
    Tytuł:
    Existence and exponential stability of a periodic solution for fuzzy cellular neural networks with time-varying delays
    Autorzy:
    Zhang, Q.
    Yang, L.
    Liao, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/930186.pdf
    Data publikacji:
    2011
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sieć neuronowa
    stateczność wykładnicza
    rozwiązanie okresowe
    fuzzy cellular neural networks
    global exponential stability
    periodic solution
    coincidence degree
    Opis:
    Fuzzy cellular neural networks with time-varying delays are considered. Some sufficient conditions for the existence and exponential stability of periodic solutions are obtained by using the continuation theorem based on the coincidence degree and the differential inequality technique. The sufficient conditions are easy to use in pattern recognition and automatic control. Finally, an example is given to show the feasibility and effectiveness of our methods.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2011, 21, 4; 649-658
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Almost periodic synchronization of fuzzy cellular neural networks with time-varying delays via state-feedback and impulsive control
    Autorzy:
    Li, Yongkun
    Wang, Huimei
    Meng, Xiaofang
    Powiązania:
    https://bibliotekanauki.pl/articles/331017.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    almost periodic solution
    fuzzy cellular neural networks
    time varying delays
    state feedback
    impulsive control
    rozwiązanie okresowe
    sieć neuronowa
    opóźnienie czasowe
    sprzężenie zwrotne
    Opis:
    In this paper, we are concerned with drive-response synchronization for a class of fuzzy cellular neural networks with time varying delays. Based on the exponential dichotomy of linear differential equations, the Banach fixed point theorem and the differential inequality technique, we obtain the existence of almost periodic solutions of this class of networks. Then, we design a state feedback and an impulsive controller, and construct a suitable Lyapunov function to study the problem of global exponential almost periodic synchronization for the drive-response systems considered. At the end of the paper, we provide an example to verify the effectiveness of the theoretical results.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2019, 29, 2; 337-349
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fuzzy and Neural Control of an Induction Motor
    Autorzy:
    Denai, M., A.
    Attia, S. A.
    Powiązania:
    https://bibliotekanauki.pl/articles/908003.pdf
    Data publikacji:
    2002
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    automatyka
    fuzzy control
    neural networks
    induction motor
    vector control
    speed observer
    Opis:
    This paper presents some design approaches to hybrid control systems combining conventional control techniques with fuzzy logic and neural networks. Such a mixed implementation leads to a more effective control design with improved system performance and robustness. While conventional control allows different design objectives such as steady state and transient characteristics of the closed loop system to be specified, fuzzy logic and neural networks are integrated to overcome the problems with uncertainties in the plant parameters and structure encountered in the classical model-based design. Induction motors are characterised by complex, highly non-linear and time-varying dynamics and inaccessibility of some states and outputs for measurements, and hence can be considered as a challenging engineering problem. The advent of vector control techniques has partially solved induction motor control problems, because they are sensitive to drive parameter variations and performance may deteriorate if conventional controllers are used. Fuzzy logic and neural network-based controllers are considered as potential candidates for such an application. Three control approaches are developed and applied to adjust the speed of the drive system. The first control design combines the variable structure theory with the fuzzy logic concept. In the second approach neural networks are used in an internal model control structure. Finally, a fuzzy state feedback controller is developed based on the pole placement technique. A simulation study of these methods is presented. The effectiveness of these controllers is demonstrated for different operating conditions of the drive system.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2002, 12, 2; 221-233
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Improving the Generalization Ability of Neuro-Fuzzy Systems by e-Insensitive Learning
    Autorzy:
    Łęski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/908037.pdf
    Data publikacji:
    2002
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    informatyka
    fuzzy systems
    neural networks
    tolerant learning
    generalization control
    robust methods
    Opis:
    A new learning method tolerant of imprecision is introduced and used in neuro-fuzzy modelling. The proposed method makes it possible to dispose of an intrinsic inconsistency of neuro-fuzzy modelling, where zero-tolerance learning is used to obtain a fuzzy model tolerant of imprecision. This new method can be called e-insensitive learning, where, in order to fit the fuzzy model to real data, the e-insensitive loss function is used. e-insensitive learning leads to a model with minimal Vapnik-Chervonenkis dimension, which results in an improved generalization ability of this system. Another advantage of the proposed method is its robustness against outliers. This paper introduces two approaches to solving e-insensitive learning problem. The first approach leads to a quadratic programming problem with bound constraints and one linear equality constraint. The second approach leads to a problem of solving a system of linear inequalities. Two computationally efficient numerical methods for e-insensitive learning are proposed. Finally, examples are given to demonstrate the validity of the introduced methods.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2002, 12, 3; 437-447
    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:
    Fusion Technology of Neural Networks and Fuzzy Systems: a Chronicled Progression from the Laboratory to Our Daily Lives
    Autorzy:
    Takagi, H.
    Powiązania:
    https://bibliotekanauki.pl/articles/911142.pdf
    Data publikacji:
    2000
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sieć neuronowa
    system rozmyty
    algorytmy
    cooperative models
    neural networks
    fuzzy systems
    genetic algorithms
    real world applications
    overview
    Opis:
    We chronicle the research on the fusion technology of neural networks and fuzzy systems (NN+FS), the models that have been proposed from this research, and the commercial products and industrial systems that have adopted these models. First, we review the NN+FS research activity during the early stages of their development in Japan, the US, and Europe. Next, following the classifi- cation of NN+FS models, we show the ease of fusing these technologies based on the similarities of the data flow network structures and the non-linearity realization strategies of NNs and FSs. Then, we describe several models and applications of NN+FS. Finally, we introduce some important and recently developed NN+FS patents.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2000, 10, 4; 647-673
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Soft computing in model-based predictive control
    Autorzy:
    Tatjewski, P.
    Ławryńczuk, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/908473.pdf
    Data publikacji:
    2006
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sterowanie procesami
    sterowanie predykcyjne
    system nieliniowy
    system rozmyty
    sieć neuronowa
    process control
    model predictive control
    nonlinear systems
    fuzzy systems
    neural networks
    Opis:
    The application of fuzzy reasoning techniques and neural network structures to model-based predictive control (MPC) is studied. First, basic structures of MPC algorithms are reviewed. Then, applications of fuzzy systems of the Takagi-Sugeno type in explicit and numerical nonlinear MPC algorithms are presented. Next, many techniques using neural network modeling to improve structural or computational properties of MPC algorithms are presented and discussed, from a neural network model of a process in standard MPC structures to modeling parts or entire MPC controllers with neural networks. Finally, a simulation example and conclusions are given.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2006, 16, 1; 7-26
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-8 z 8

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