Non-Linear Feedback Neural Networks

Beschreibung

This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known that 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.

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.
First dedicated book on non-linear feedback neural networks Contains thorough discussion on transcendental energy function Includes special chapter on Hopfield Network, its applications, and limitations Cadence OrCAD circuit files for all the circuit simulations discussed in the book Useful material for researchers working in the area of analog computation

Autor*in

Mohd. Samar Ansari

Themen in »Non-Linear Feedback Neural Networks«

Graph Coloring Hardware Simulation Hopfield Neural Network Mathematical Programming Neural Networks Non-Linear Feedback Transcendental Energy Function

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