Adaptive learning to control chaos

Author

Rhode, M.A. ; Rollins, R.W. ; Vassiliadis, C.A.

Author_Institution

Dept. of Phys., Ohio Univ., Athens, OH, USA

fYear

1994

fDate

20-22 Mar 1994

Firstpage

638

Lastpage

642

Abstract

Presents a method of adaptive learning to control chaos. It is a composite of artificial neural networks and the approach of Ott, Grebogi and Yorke (OGY) (1990) to control unstable periodic orbits in deterministic chaotic systems. The authors implement an OGY type control using a simple linear feedforward network or perceptron and present a method of learning to continuously update the control strategy. To realize supervised learning, the authors least square fit the weights of the perceptron according to the behavior of the system and its response to the control signals. The logistic map and a three dimensional model of an electrochemical system are used as examples

Keywords

adaptive control; chaos; feedforward neural nets; learning (artificial intelligence); least squares approximations; adaptive learning; artificial neural networks; chaos; deterministic chaotic systems; electrochemical system; least square fitting; linear feedforward network; logistic map; perceptron; supervised learning; three dimensional model; unstable periodic orbits; Adaptive control; Chaos; Control systems; Extraterrestrial measurements; Least squares methods; Logistics; Orbits; Physics; Programmable control; Proportional control;

fLanguage

English

Publisher

ieee

Conference_Titel

System Theory, 1994., Proceedings of the 26th Southeastern Symposium on

Conference_Location

Athens, OH

ISSN

0094-2898

Print_ISBN

0-8186-5320-5

Type

conf

DOI

10.1109/SSST.1994.287799

Filename

287799