Identifying nonlinear dynamic systems using neural nets and evolutionary programming

Author

Hutchins, R.G.

Author_Institution

Dept. of Electr. & Comput. Eng., Naval Postgraduate Sch., Monterey, CA, USA

Volume

2

fYear

1994

fDate

31 Oct-2 Nov 1994

Firstpage

887

Abstract

Nonlinear system behavior is not always well characterized by linearized system models, especially if the system is chaotic. This research studies the use of a neural network algorithm structure to model two nonlinear systems, a quadratic system and a chaotic system. An evolutionary programming approach is employed to train the neural nets so that the training process might better avoid selecting weighting parameters that represent a local minimum rather than a global minimum. This training approach is compared with the more standard backpropagation technique

Keywords

feedforward neural nets; genetic algorithms; identification; learning (artificial intelligence); multilayer perceptrons; nonlinear dynamical systems; backpropagation technique; chaotic system; evolutionary programming; multilayer perceptron; neural nets; neural network algorithm structure; nonlinear dynamic systems identification; nonlinear system behavior; quadratic system; training process; weighting parameters; Chaos; Dynamic programming; Equations; Genetic programming; Network topology; Neural networks; Nonlinear dynamical systems; Nonlinear systems; Sampling methods; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 1994. 1994 Conference Record of the Twenty-Eighth Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

0-8186-6405-3

Type

conf

DOI

10.1109/ACSSC.1994.471588

Filename

471588