Non-linear feedback neural networks [electronic resource] : VLSI implementations and applications / Mohd. Samar Ansari.
Ansari, Mohd. Samar, author.
2013
QA76.87
Available Online

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Title
Non-linear feedback neural networks [electronic resource] : VLSI implementations and applications / Mohd. Samar Ansari.
Author
Ansari, Mohd. Samar, author.
ISBN
9788132215639 electronic book
813221563X electronic book
9788132215622
DOI
https://doi.org/10.1007/978-81-322-1563-9
Published
New Delhi : Springer, [2013?]
Copyright
©2014
Language
English
Description
1 online resource (xxii, 201 pages) : illustrations.
Item Number
10.1007/978-81-322-1563-9 doi
Call Number
QA76.87
Dewey Decimal Classification
006.3/2
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Description based on online resource; title from PDF title page (SpringerLink, viewed September 9, 2013).
Series
Studies in computational intelligence ; v.508.
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Voltage-mode Neural Network for the Solution of Linear Equations
Mixed-mode Neural Circuit for Solving Linear Equations
Non-Linear Feedback Neural Circuits for Linear and Quadratic Programming
OTA-based Implementations of Mixed-mode Neural Circuits
Appendix A: Mixed-mode Neural Network for Graph Colouring
Appendix B: Mixed-mode Neural Network for Ranking.

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