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Complex Valued Neural Networks Multi Valued by Aizenberg Igor (22 results)
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Hardcover. Condition: As New. No Jacket. Pages are clean and are not marred by notes or folds of any kind. ~ ThriftBooks: Read More, Spend Less 1.35.
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Complex-Valued Neural Networks with Multi-Valued Neurons (Studies in Computational Intelligence, 353)
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Complex-Valued Neural Networks with Multi-Valued Neurons (Studies in Computational Intelligence, 353)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Condition: New. pp. 280.
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Condition: New. pp. 277.
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Complex-Valued Neural Networks with Multi-Valued Neurons
Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg Aug 2016, 2016
ISBN 10: 3662506319 ISBN 13: 9783662506318
Language: English
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US$ 193.74
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Add to basketTaschenbuch. Condition: 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.
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Complex-Valued Neural Networks with Multi-Valued Neurons
Published by Springer Berlin Heidelberg, 2016
ISBN 10: 3662506319 ISBN 13: 9783662506318
Language: English
US$ 193.74
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Add to basketTaschenbuch. Condition: 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.
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US$ 268.14
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US$ 274.64
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Add to basketHardcover. Condition: Brand New. 2011 edition. 280 pages. 9.50x6.50x0.75 inches. In Stock.
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Complex-Valued Neural Networks with Multi-Valued Neurons
Published by Springer Berlin Heidelberg Aug 2016, 2016
ISBN 10: 3662506319 ISBN 13: 9783662506318
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
US$ 193.74
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Add to basketTaschenbuch. Condition: 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.
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Complex-Valued Neural Networks with Multi-Valued Neurons
Published by Springer Berlin Heidelberg, 2011
ISBN 10: 3642203523 ISBN 13: 9783642203527
Language: English
US$ 165.70
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Add to basketGebunden. Condition: 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.
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Complex-Valued Neural Networks with Multi-Valued Neurons
Published by Springer Berlin Heidelberg, 2016
ISBN 10: 3662506319 ISBN 13: 9783662506318
Language: English
US$ 164.37
Convert currencyUS$ 57.42 shipping from Germany to U.S.A.Quantity: Over 20 available
Add to basketCondition: 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.
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US$ 252.04
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Add to basketCondition: New. Print on Demand pp. 277.
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US$ 274.81
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Add to basketCondition: New. PRINT ON DEMAND pp. 277.
