Identification of Nonlinear Dynamic Systems Using Neural Networks

[+] Author and Article Information
S. F. Masri

Department of Civil Engineering, University of Southern California, Los Angeles, CA 90089

A. G. Chassiakos

School of Engineering, California State University, Long Beach, CA 90840

T. K. Caughey

Division of Engineering, California Institute of Technology, Pasadena, CA 91125

J. Appl. Mech 60(1), 123-133 (Mar 01, 1993) (11 pages) doi:10.1115/1.2900734 History: Received April 10, 1991; Revised November 15, 1991; Online March 31, 2008


A procedure based on the use of artificial neural networks for the identification of nonlinear dynamic systems is developed and applied to the damped Duffing oscillator under deterministic excitation. The “generalization” ability of neural networks is invoked to predict the response of the same nonlinear oscillator under stochastic excitations of differing magnitude. The analogy between the neural network approach and a qualitatively similar nonparametric identification technique previously developed by the authors is illustrated. Some of the computational aspects of identification by neural networks, as well as their fault-tolerant nature, are discussed. It is shown that neural networks provide high-fidelity mathematical models of structure-unknown nonlinear systems encountered in the applied mechanics field.

Copyright © 1993 by The American Society of Mechanical Engineers
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