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Proceedings Paper

Evolved functional neural networks for system identification
Author(s): Hector Erives; Ramon Parra-Loera
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Paper Abstract

A new approach to the identification of dynamical systems by means of evolved neural networks is presented. We implement two functional neural networks: polynomials and orthogonal basis functions. The functional neural networks contain four parameters that need to be optimized: the weights, training parameters, network topology and scaling factors. An approach to the solution of this combinatorial problem is to genetically evolve functional neural networks. This paper presents a preliminary analysis of the proposed method to automatically select network parameters. The networks are encoded as chromosomes that are evolved during the identification by means of genetic algorithms. Experimental results show that the method is effective for the identification of dynamical systems.

Paper Details

Date Published: 14 June 1996
PDF: 8 pages
Proc. SPIE 2755, Signal Processing, Sensor Fusion, and Target Recognition V, (14 June 1996); doi: 10.1117/12.243201
Show Author Affiliations
Hector Erives, New Mexico State Univ. (United States)
Ramon Parra-Loera, New Mexico State Univ. (United States)
Univ. Autonoma de Ciudad Juarez (Mexico)

Published in SPIE Proceedings Vol. 2755:
Signal Processing, Sensor Fusion, and Target Recognition V
Ivan Kadar; Vibeke Libby, Editor(s)

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