Feed-Forward Neural Networks

Vector Decomposition Analysis, Modelling and Analog Implementation

By: Jouke Annema

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Hardcover | 31 May 1995

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256 Pages

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24.77 x 16.51 x 1.91

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Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation presents a novel method for the mathematical analysis of neural networks that learn according to the back-propagation algorithm. The book also discusses some other recent alternative algorithms for hardware implemented perception-like neural networks. The method permits a simple analysis of the learning behaviour of neural networks, allowing specifications for their building blocks to be readily obtained.
Starting with the derivation of a specification and ending with its hardware implementation, analog hard-wired, feed-forward neural networks with on-chip back-propagation learning are designed in their entirety. On-chip learning is necessary in circumstances where fixed weight configurations cannot be used. It is also useful for the elimination of most mis-matches and parameter tolerances that occur in hard-wired neural network chips.
Fully analog neural networks have several advantages over other implementations: low chip area, low power consumption, and high speed operation.
Feed-Forward Neural Networks is an excellent source of reference and may be used as a text for advanced courses.

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Non-Fiction Computing & I.T.Computer Programming & Software Development Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Science Physics Electricity Engineering & Technology Energy Technology & Engineering Electrical Engineering

Electronics & Communications Engineering Electronics Engineering Circuits & Components Mechanical Engineering & Materials Mechanical Engineering

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Published: 10th November 2013

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Foreword
Acknowledgments
Introduction	p. 1
The Vector Decomposition Method	p. 27
Dynamics of Single Layer Nets	p. 39
Unipolar Input Signals in Single-Layer Feed-Forward Neural Networks	p. 57
Cross-talk in Single-Layer Feed-Forward Neural Networks	p. 63
Precision Requirements for Analog Weight Adaptation Circuitry for Single-Layer Nets	p. 75
Discretization of Weight Adaptations in Single-Layer Nets	p. 91
Learning Behavior and Temporary Minima of Two-Layer Neural Networks	p. 107
Biases and Unipolar Input signals for Two-Layer Neural Networks	p. 147
Cost Functions for Two-Layer Neural Networks	p. 167
Some issues for f'(x)	p. 177
Feed-forward hardware	p. 187
Analog weight adaptation hardware	p. 215
Conclusions	p. 229
Index	p. 235
Nomenclature	p. 237
Table of Contents provided by Blackwell. All Rights Reserved.

Feed-Forward Neural Networks

Vector Decomposition Analysis, Modelling and Analog Implementation

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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