Complex-Valued Neural Networks with Multi-Valued Neurons - Couverture souple

Aizenberg, Igor

9783662506318: Complex-Valued Neural Networks with Multi-Valued Neurons

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ISBN 10 : 3662506319 ISBN 13 : 9783662506318

Editeur : Springer, 2016

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Complex-Valued Neural Networks have higher functionality, learn faster and generalize better than their real-valued counterparts.

This book is devoted to the Multi-Valued Neuron (MVN) and MVN-based neural networks. It contains a comprehensive observation of MVN theory, its learning, and applications. MVN is a complex-valued neuron whose inputs and output are located on the unit circle. Its activation function is a function only of argument (phase) of the weighted sum. MVN derivative-free learning is based on the error-correction rule. A single MVN can learn those input/output mappings that are non-linearly separable in the real domain. Such classical non-linearly separable problems as XOR and Parity n are the simplest that can be learned by a single MVN. Another important advantage of MVN is a proper treatment of the phase information.

These properties of MVN become even more remarkable when this neuron is used as a basic one in neural networks. The Multilayer Neural Network based on Multi-Valued Neurons (MLMVN) is an MVN-based feedforward neural network. Its backpropagation learning algorithm is derivative-free and based on the error-correction rule. It does not suffer from the local minima phenomenon. MLMVN outperforms many other machine learning techniques in terms of learning speed, network complexity and generalization capability when solving both benchmark and real-world classification and prediction problems. Another interesting application of MVN is its use as a basic neuron in multi-state associative memories.

The book is addressed to those readers who develop theoretical fundamentals of neural networks and use neural networks for solving various real-world problems. It should also be very suitable for Ph.D. and graduate students pursuing their degrees in computational intelligence.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

Pr�sentation de l'�diteur

Complex-valued neural networks have higher functionality, learn faster and generalize better than their real-valued counterparts. This book on the multi-valued neuron (MVN) and MVN-based neural networks covers MVN theory, learning, and applications.

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteurSpringer
Date d'�dition2016
ISBN 10 3662506319
ISBN 13 9783662506318
ReliureBroch�
Langueanglais
Nombre de pages280
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Complex-Valued Neural Networks with Multi-Valued Neurons

Igor Aizenberg

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ISBN 10 : 3662506319 ISBN 13 : 9783662506318

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Cutting-edge research on Complex-Valued Networks with Multi-Valued NeuronsWritten by leading experts in this fieldState-of-the-Art bookComplex-Valued Neural Networks have higher functionality, learn faster and generalize b. N� de r�f. du vendeur 385771721

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Complex-Valued Neural Networks with Multi-Valued Neurons

Igor Aizenberg

Edit� par Springer Berlin Heidelberg, 2016

ISBN 10 : 3662506319 ISBN 13 : 9783662506318

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Complex-Valued Neural Networks have higher functionality, learn faster and generalize better than their real-valued counterparts.This book is devoted to the Multi-Valued Neuron (MVN) and MVN-based neural networks. It contains a comprehensive observation of MVN theory, its learning, and applications. MVN is a complex-valued neuron whose inputs and output are located on the unit circle. Its activation function is a function only of argument (phase) of the weighted sum. MVN derivative-free learning is based on the error-correction rule. A single MVN can learn those input/output mappings that are non-linearly separable in the real domain. Such classical non-linearly separable problems as XOR and Parity n are the simplest that can be learned by a single MVN. Another important advantage of MVN is a proper treatment of the phase information.These properties of MVN become even more remarkable when this neuron is used as a basic one in neural networks. The Multilayer Neural Network based on Multi-Valued Neurons (MLMVN) is an MVN-based feedforward neural network. Its backpropagation learning algorithm is derivative-free and based on the error-correction rule. It does not suffer from the local minima phenomenon. MLMVN outperforms many other machine learning techniques in terms of learning speed, network complexity and generalization capability when solving both benchmark and real-world classification and prediction problems. Another interesting application of MVN is its use as a basic neuron in multi-state associative memories. The book is addressed to those readers who develop theoretical fundamentals of neural networks and use neural networks for solving various real-world problems. It should also be very suitable for Ph.D. and graduate students pursuing their degrees in computational intelligence. N� de r�f. du vendeur 9783662506318

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Complex-Valued Neural Networks with Multi-Valued Neurons

Igor Aizenberg

Edit� par Springer Berlin Heidelberg Aug 2016, 2016

ISBN 10 : 3662506319 ISBN 13 : 9783662506318

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Complex-Valued Neural Networks have higher functionality, learn faster and generalize better than their real-valued counterparts.This book is devoted to the Multi-Valued Neuron (MVN) and MVN-based neural networks. It contains a comprehensive observation of MVN theory, its learning, and applications. MVN is a complex-valued neuron whose inputs and output are located on the unit circle. Its activation function is a function only of argument (phase) of the weighted sum. MVN derivative-free learning is based on the error-correction rule. A single MVN can learn those input/output mappings that are non-linearly separable in the real domain. Such classical non-linearly separable problems as XOR and Parity n are the simplest that can be learned by a single MVN. Another important advantage of MVN is a proper treatment of the phase information.These properties of MVN become even more remarkable when this neuron is used as a basic one in neural networks. The Multilayer Neural Network based on Multi-Valued Neurons (MLMVN) is an MVN-based feedforward neural network. Its backpropagation learning algorithm is derivative-free and based on the error-correction rule. It does not suffer from the local minima phenomenon. MLMVN outperforms many other machine learning techniques in terms of learning speed, network complexity and generalization capability when solving both benchmark and real-world classification and prediction problems. Another interesting application of MVN is its use as a basic neuron in multi-state associative memories. The book is addressed to those readers who develop theoretical fundamentals of neural networks and use neural networks for solving various real-world problems. It should also be very suitable for Ph.D. and graduate students pursuing their degrees in computational intelligence. 280 pp. Englisch. N� de r�f. du vendeur 9783662506318

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Complex-Valued Neural Networks with Multi-Valued Neurons (Studies in Computational Intelligence, 353)

Aizenberg, Igor

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ISBN 10 : 3662506319 ISBN 13 : 9783662506318

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Complex-Valued Neural Networks with Multi-Valued Neurons

Igor Aizenberg

Edit� par Springer Berlin Heidelberg, Springer Berlin Heidelberg Aug 2016, 2016

ISBN 10 : 3662506319 ISBN 13 : 9783662506318

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Taschenbuch. Etat : Neu. Neuware -Complex-Valued Neural Networks have higher functionality, learn faster and generalize better than their real-valued counterparts.This book is devoted to the Multi-Valued Neuron (MVN) and MVN-based neural networks. It contains a comprehensive observation of MVN theory, its learning, and applications. MVN is a complex-valued neuron whose inputs and output are located on the unit circle. Its activation function is a function only of argument (phase) of the weighted sum. MVN derivative-free learning is based on the error-correction rule. A single MVN can learn those input/output mappings that are non-linearly separable in the real domain. Such classical non-linearly separable problems as XOR and Parity n are the simplest that can be learned by a single MVN. Another important advantage of MVN is a proper treatment of the phase information.These properties of MVN become even more remarkable when this neuron is used as a basic one in neural networks. The Multilayer Neural Network based on Multi-Valued Neurons (MLMVN) is an MVN-based feedforward neural network. Its backpropagation learning algorithm is derivative-free and based on the error-correction rule. It does not suffer from the local minima phenomenon. MLMVN outperforms many other machine learning techniques in terms of learning speed, network complexity and generalization capability when solving both benchmark and real-world classification and prediction problems. Another interesting application of MVN is its use as a basic neuron in multi-state associative memories.The book is addressed to those readers who develop theoretical fundamentals of neural networks and use neural networks for solving various real-world problems. It should also be very suitable for Ph.D. and graduate students pursuing their degrees in computational intelligence.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 280 pp. Englisch. N� de r�f. du vendeur 9783662506318

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