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Proceedings Paper

Proportional integral tuning rules for adaptive neural networks
Author(s): Steven C. Rogers
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Paper Abstract

Tuning rules for adaptive neural networks have featured Lyapunov-based approaches in recent years. Although these have some desirable qualities they have led to complex tuning procedures. In order to take more advantage of the power of adaptive neural networks less complex and computationally expensive tuning rules are desirable. In addition, tuning rules should be simple and provide for rapid, reliable convergence. In this paper a proportional-integral approach to adaptive neural network tuning rules is studied. Simulation on a nonlinear system is used for a demonstration.

Paper Details

Date Published: 4 August 2003
PDF: 7 pages
Proc. SPIE 5103, Intelligent Computing: Theory and Applications, (4 August 2003); doi: 10.1117/12.485783
Show Author Affiliations
Steven C. Rogers, Institute for Scientific Research, Inc. (United States)


Published in SPIE Proceedings Vol. 5103:
Intelligent Computing: Theory and Applications
Kevin L. Priddy; Peter J. Angeline, Editor(s)

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