This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known the standard HNN suffers from problems of convergence to local minima, and requirement of a large number of neurons and synaptic weights. Therefore, improved solutions are needed. The non-linear synapse neural network (NoSyNN) is one such possibility and is discussed in detail in this book. This book also discusses the applications in computationally intensive tasks like graph coloring, ranking, ...
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This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known the standard HNN suffers from problems of convergence to local minima, and requirement of a large number of neurons and synaptic weights. Therefore, improved solutions are needed. The non-linear synapse neural network (NoSyNN) is one such possibility and is discussed in detail in this book. This book also discusses the applications in computationally intensive tasks like graph coloring, ranking, and linear as well as quadratic programming. The material in the book is useful to students, researchers and academician working in the area of analog computation.
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Add this copy of Non-Linear Feedback Neural Networks to cart. $58.99, very good condition, Sold by Russell Books rated 4.0 out of 5 stars, ships from Victoria, BC, CANADA, published 2013 by Springer, India, Private Ltd.
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Add this copy of Non-Linear Feedback Neural Networks: VLSI to cart. $112.32, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2013 by Springer, India, Private Ltd.
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New. Print on demand Contains: Illustrations, black & white. Studies in Computational Intelligence . XXII, 201 p. 79 illus. Intended for professional and scholarly audience.
