pattern association reference book de kumar somesh (7 résultats)

Paperback. Etat : Brand New. 108 pages. 8.66x5.91x0.25 inches. In Stock.

Taschenbuch. Etat : Neu. Pattern Association: A Reference Book | A hybrid approach for pattern association | Somesh Kumar | Taschenbuch | 108 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783330021198 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In the present book, our objective is to develop a hybrid evolutionary system consisting of Hopfield neural network and genetic algorithm which will be responsible for evolution of weight matrices in order to store some input patterns and analyze the performance of such a system in the terms of correct recalling of these already stored patterns again with evolutionary algorithm by presenting the same or noisy versions of input patterns. In this process, first the patterns of training set have been encoded in the neural network using MC-adaptation rule. It is expected that all the patterns of training set has been successfully stored as the associative memory feature of Hopfield type neural network. As a result of this learning process, we obtain the expected optimized weight matrices. Now, we employ the genetic algorithm to evolve the population of these approximate optimal weight matrices obtained by MC-adaptation rule. The fitness of every evolved population of weight matrices is evaluated by using the two fitness evaluation functions. 108 pp. Englisch.

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kumar SomeshDr. Somesh Kumar presently spearheads the MCA as a Director and IT department in NIET, Greater Noida, India. He has completed his MCA degree in 2000, ME (CS&E) in 2006, and Ph.D(CS) in 2011. Since 2000, he is in teaching .

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -In the present book, our objective is to develop a hybrid evolutionary system consisting of Hopfield neural network and genetic algorithm which will be responsible for evolution of weight matrices in order to store some input patterns and analyze the performance of such a system in the terms of correct recalling of these already stored patterns again with evolutionary algorithm by presenting the same or noisy versions of input patterns. In this process, first the patterns of training set have been encoded in the neural network using MC-adaptation rule. It is expected that all the patterns of training set has been successfully stored as the associative memory feature of Hopfield type neural network. As a result of this learning process, we obtain the expected optimized weight matrices. Now, we employ the genetic algorithm to evolve the population of these approximate optimal weight matrices obtained by MC-adaptation rule. The fitness of every evolved population of weight matrices is evaluated by using the two fitness evaluation functions.Books on Demand GmbH, Überseering 33, 22297 Hamburg 108 pp. Englisch.

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In the present book, our objective is to develop a hybrid evolutionary system consisting of Hopfield neural network and genetic algorithm which will be responsible for evolution of weight matrices in order to store some input patterns and analyze the performance of such a system in the terms of correct recalling of these already stored patterns again with evolutionary algorithm by presenting the same or noisy versions of input patterns. In this process, first the patterns of training set have been encoded in the neural network using MC-adaptation rule. It is expected that all the patterns of training set has been successfully stored as the associative memory feature of Hopfield type neural network. As a result of this learning process, we obtain the expected optimized weight matrices. Now, we employ the genetic algorithm to evolve the population of these approximate optimal weight matrices obtained by MC-adaptation rule. The fitness of every evolved population of weight matrices is evaluated by using the two fitness evaluation functions.