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Proceedings Paper

New on-line adaptive algorithm for nonlinear system identification and control
Author(s): Girish Govind; P. Ramamoorthy
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Paper Abstract

Neural-based nonlinear system identification and control suffers from the problem of slow convergence, and selection of a suitable architecture for a problem is made through trial and error. There is the need for an algorithm that would provide an efficient solution to these problems. This paper presents one possible solution. Unlike the backpropagation algorithm that trains a fixed structure, in the algorithm presented in this paper, the network is built slowly in a step-by-step fashion. This evolving architecture methodology permits an optimal allocation of hidden nodes that avoids training on outliers and at the same time, provides sufficient complexity for the approximation of a data set. Through simulation examples we show that this algorithm also exhibits faster convergence properties than the usual multi- layered neural network algorithms. Finally, we examine some common ideas between this architecture and fuzzy logic systems.

Paper Details

Date Published: 20 August 1992
PDF: 11 pages
Proc. SPIE 1706, Adaptive and Learning Systems, (20 August 1992); doi: 10.1117/12.139943
Show Author Affiliations
Girish Govind, Univ. of Cincinnati (United States)
P. Ramamoorthy, Univ. of Cincinnati (United States)

Published in SPIE Proceedings Vol. 1706:
Adaptive and Learning Systems
Firooz A. Sadjadi, Editor(s)

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