Artificial Neural Network Training & Software Implementation Techniques

By: Ali Kattan, Zong Woo Geem, Rosni Abdullah

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Paperback | 14 June 2011

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58 Pages

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Artificial neural networks (ANN) are widely used in diverse fields of science and industry. Though there have been numerous techniques used for their implementations, the choice of a specific implementation is subjected to different factors including cost, accuracy, processing speed and overall performance. Featured with synaptic plasticity, the process of training is concerned with adjusting the individual weights between each of the individual ANN neurons until we can achieve close to the desired output. This book introduces the common trajectory-driven and evolutionary-based ANN training algorithms.

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Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Computer Networking & Communications

Introduction
Feed-Forward Neural Networks
FFANN Software Simulation
FFANN Training Concept
Trajectory-Driven Training Paradigm
Evolutionary-Based Training Paradigm
FFANN Simulation Utilizing Graphic-Processing Units
Conclusion
Index
Table of Contents provided by Publisher. All Rights Reserved.

Artificial Neural Network Training & Software Implementation Techniques

At a Glance

Paperback

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How to return your order

Defective items

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