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Implementation and performance evaluation of the agent-based algorithm for ANN training

Implementation and performance evaluation of the agent-based algorithm for ANN training The paper contains a description of the implementation and performance evaluation of the agent-based population learning algorithm used to train the feed-forward artificial neural networks. The goal of the research was to evaluate efficiency of the agent-based approach and to establish experimentally which different factors representing the A-Team structure and topology affect the performance of the analyzed agent-based algorithm. The paper includes a general overview of the JABAT environment used to deploy the ANN training algorithm, a description of different agents employed and their roles, as well as the computational experiment plan and the discussion of the performance evaluation results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Knowledge-Based and Intelligent Engineering Systems IOS Press

Implementation and performance evaluation of the agent-based algorithm for ANN training

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Publisher
IOS Press
Copyright
Copyright © 2010 by IOS Press, Inc
ISSN
1327-2314
eISSN
1875-8827
DOI
10.3233/KES-2010-0185
Publisher site
See Article on Publisher Site

Abstract

The paper contains a description of the implementation and performance evaluation of the agent-based population learning algorithm used to train the feed-forward artificial neural networks. The goal of the research was to evaluate efficiency of the agent-based approach and to establish experimentally which different factors representing the A-Team structure and topology affect the performance of the analyzed agent-based algorithm. The paper includes a general overview of the JABAT environment used to deploy the ANN training algorithm, a description of different agents employed and their roles, as well as the computational experiment plan and the discussion of the performance evaluation results.

Journal

International Journal of Knowledge-Based and Intelligent Engineering Systems – IOS Press

Published: Jan 1, 2010

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