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Record Nr. |
UNINA9910254243603321 |
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Autore |
Gaxiola Fernando |
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Titolo |
New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks [[electronic resource] /] / by Fernando Gaxiola, Patricia Melin, Fevrier Valdez |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
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ISBN |
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Edizione |
[1st ed. 2016.] |
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Descrizione fisica |
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1 online resource (111 p.) |
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Collana |
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SpringerBriefs in Computational Intelligence, , 2625-3704 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Artificial intelligence |
Computational Intelligence |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters and index. |
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Nota di contenuto |
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Introduction.-Theory and Background -- Problem Statement an Development -- Simulations and Results -- Conclusions. |
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Sommario/riassunto |
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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 รด=17) and Dow-Jones time series, and recognition of person with iris biometric measure. In some |
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