Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Proposes a neural network learning method with type-2 fuzzy weight adjustment Presents a mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weightsPresents simulation results and a comparati. N� de r�f. du vendeur 119612055

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New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks

Fernando Gaxiola

Edit� par Springer International Publishing, 2016

ISBN 10 : 3319340867 ISBN 13 : 9783319340869

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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - In this book a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially fuzzy weights.The internal operation of the neuron is changed to work with two internal calculations for the activation function to obtain two results as outputs of the proposed method. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method.The proposed approach is based on recent methods that handle adaptation of weights using fuzzy logic of type-1 and type-2. The proposed approach is applied to a cases of prediction for the Mackey-Glass (for �=17) and Dow-Jones time series, and recognition of person with iris biometric measure. In some experiments, noise was applied in different levels to the test data of the Mackey-Glass time series for showing that the type-2 fuzzy backpropagation approach obtains better behavior and tolerance to noise than the other methods.The optimization algorithms that were used are the genetic algorithm and the particle swarm optimization algorithm and the purpose of applying these methods was to find the optimal type-2 fuzzy inference systems for the neural network with type-2 fuzzy weights that permit to obtain the lowest prediction error. N� de r�f. du vendeur 9783319340869

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New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks

Fernando Gaxiola

Edit� par Springer International Publishing Jun 2016, 2016

ISBN 10 : 3319340867 ISBN 13 : 9783319340869

Neuf Taschenbuch

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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In this book a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially fuzzy weights.The internal operation of the neuron is changed to work with two internal calculations for the activation function to obtain two results as outputs of the proposed method. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method.The proposed approach is based on recent methods that handle adaptation of weights using fuzzy logic of type-1 and type-2. The proposed approach is applied to a cases of prediction for the Mackey-Glass (for �=17) and Dow-Jones time series, and recognition of person with iris biometric measure. In some experiments, noise was applied in different levels to the test data of the Mackey-Glass time series for showing that the type-2 fuzzy backpropagation approach obtains better behavior and tolerance to noise than the other methods.The optimization algorithms that were used are the genetic algorithm and the particle swarm optimization algorithm and the purpose of applying these methods was to find the optimal type-2 fuzzy inference systems for the neural network with type-2 fuzzy weights that permit to obtain the lowest prediction error. 112 pp. Englisch. N� de r�f. du vendeur 9783319340869

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New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks (SpringerBriefs in Computational Intelligence)

Gaxiola, Fernando; Melin, Patricia; Valdez, Fevrier

Edit� par Springer, 2016

ISBN 10 : 3319340867 ISBN 13 : 9783319340869

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Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni

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Etat : New. In. N� de r�f. du vendeur ria9783319340869_new

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New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks

Fernando Gaxiola

Edit� par Springer International Publishing, Springer International Publishing Jun 2016, 2016

ISBN 10 : 3319340867 ISBN 13 : 9783319340869

Neuf Taschenbuch

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. Neuware -In this book a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially fuzzy weights.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 112 pp. Englisch. N� de r�f. du vendeur 9783319340869

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