Evolved functional neural networks for system identification

Hector Erives; Ramon Parra-Loera

doi:10.1117/12.243201

14 June 1996 Evolved functional neural networks for system identification

Hector Erives, Ramon Parra-Loera

Proceedings Volume 2755, Signal Processing, Sensor Fusion, and Target Recognition V; (1996) https://doi.org/10.1117/12.243201
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

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.

Citation Download Citation

Hector Erives and Ramon Parra-Loera "Evolved functional neural networks for system identification", Proc. SPIE 2755, Signal Processing, Sensor Fusion, and Target Recognition V, (14 June 1996); https://doi.org/10.1117/12.243201

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available