Non-Linear Feedback Neural Networks: VLSI Implementations and Applications

✍ Scribed by Mohd. Samar Ansari (auth.)

Publisher: Springer India
Year: 2014
Tongue: English
Leaves: 217
Series: Studies in Computational Intelligence 508
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xxii
Introduction....Pages 1-11
Background....Pages 13-54
Voltage-Mode Neural Network for the Solution of Linear Equations....Pages 55-104
Mixed-Mode Neural Circuit for Solving Linear Equations....Pages 105-144
Non-Linear Feedback Neural Circuits for Linear and Quadratic Programming....Pages 145-170
OTA-Based Implementations of Mixed-Mode Neural Circuits....Pages 171-186
Conclusion....Pages 187-190
Back Matter....Pages 191-201

✦ Subjects

Computational Intelligence; Circuits and Systems; Mathematical Models of Cognitive Processes and Neural Networks; Electronics and Microelectronics, Instrumentation

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