RBF neural network based H synchronization for unknown chaotic systems

Research output: Contribution to journalArticlepeer-review


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
Issue number4
Publication statusPublished - 2010
Externally publishedYes


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

ASJC Scopus subject areas

  • General


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