RBF neural network based H synchronization for unknown chaotic systems

Research output: Contribution to journalArticle

Abstract

In this paper, we propose a new H synchronization strategy, called a Radial Basis Function Neural NetworkH synchronization (RBFNNHS) strategy, for unknown chaotic systems in the presence of external disturbance. In the proposed framework, a radial basis function neural network (RBFNN) is constructed as an alternative to approximate the unknown nonlinear function of the chaotic system. Based on this neural network and linear matrix inequality (LMI) formulation, the RBFNNHS controller and the learning laws are presented to reduce the effect of disturbance to an H norm constraint. It is shown that finding the RBFNNHS controller and the learning laws can be transformed into the LMI problem and solved using the convex optimization method. A numerical example is presented to demonstrate the validity of the proposed RBFNNHS scheme.

Original languageEnglish
Pages (from-to)449-460
Number of pages12
JournalSadhana - Academy Proceedings in Engineering Sciences
Volume35
Issue number4
DOIs
Publication statusPublished - 2010 Oct 21
Externally publishedYes

Fingerprint

Chaotic systems
Synchronization
Neural networks
Linear matrix inequalities
Controllers
Convex optimization

Keywords

  • H synchronization
  • learning law
  • linear matrix inequality (LMI)
  • radial basis function neural network (RBFNN)
  • unknown chaotic systems

ASJC Scopus subject areas

  • General

Cite this

RBF neural network based H synchronization for unknown chaotic systems. / Ahn, Choon Ki.

In: Sadhana - Academy Proceedings in Engineering Sciences, Vol. 35, No. 4, 21.10.2010, p. 449-460.

Research output: Contribution to journalArticle

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