9789810216139 - neural fuzzy control systems with structure and parameter learning de lin, chin-teng (4 resultados)
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Librería: Jeffrey Blake, Willow Grove, PA, Estados Unidos de AmericaJeffrey Blake
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EUR 28,77
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Hardcover. Condición: As New. Estado de la sobrecubierta: Very Good.
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Librería: Better World Books Ltd, Dunfermline, Reino UnidoBetter World Books Ltd
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EUR 30,24
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Condición: Good. Former library copy. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.
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Librería: Basi6 International, Irving, TX, Estados Unidos de AmericaBasi6 International
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EUR 47,02
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Condición: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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Librería: Buchpark, Trebbin, AlemaniaBuchpark
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EUR 32,34
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Condición: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | A general neural-network-based connectionist model, called Fuzzy Neural Network (FNN), is proposed in this book for the realization of a fuzzy logic control and decision system. The FNN is a feedforward multi-layered network which integrates the ba…sic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities.In order to set up this proposed FNN, the author recommends two complementary structure/parameter learning algorithms: a two-phase hybrid learning algorithm and an on-line supervised structure/parameter learning algorithm.Both of these learning algorithms require exact supervised training data for learning. In some real-time applications, exact training data may be expensive or even impossible to get. To solve this reinforcement learning problem for real-world applications, a Reinforcement Fuzzy Neural Network (RFNN) is further proposed. Computer simulation examples are presented to illustrate the performance and applicability of the proposed FNN, RFNN and their associated learning algorithms for various applications.
