Identifying chaotic systems using wiener and hammerstein cascade models

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)483-493
Journal / PublicationMathematical and Computer Modelling
Volume33
Issue number4-5
Publication statusPublished - 2001
Externally publishedYes

Abstract

This paper describes two basic structures for identifying chaotic systems based on the Wiener and Hammerstein cascade models, in which three-layer feedforward artificial neural network is employed as the nonlinear static subsystem and a simple linear plant is used as the dynamic subsystem. Through training of the neural network and choosing an appropriate linear subsystem, various chaotic systems can be well identified by these two basic structures. Computer simulation results on Henon and Lozi systems are presented to demonstrate the effectiveness of these proposed structures. It is also shown that two chaotic systems whose outputs are different can actually exhibit similar chaotic attractors. © 2001 Elsevier Science Ltd.

Research Area(s)

  • Attractor, Chaos, Identification, Neural network, Time series