Chaos control on universal learning networks

Kotaro Hirasawa, Junichi Murata, Jinglu Hu, Chunzhi Jin

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


A new chaos control method is proposed which is useful for taking advantage of chaos and avoiding it. The proposed method is based on the following facts: 1) chaotic phenomena can be generated and eliminated by controlling maximum Lyapunov exponent of systems and 2) maximum Lyapunov exponent can be formulated and calculated by using higher order derivatives of Universal Learning Networks (ULN's). ULN's consist of a number of inter-connected nodes where the nodes may have any continuously differentiable nonlinear functions in them and each pair of nodes can be connected by multiple branches with arbitrary time delays. A generalized learning algorithm has been derived for the ULN's, in which both the first-order derivatives (gradients) and the higher order derivatives are incorporated. In simulations, parameters of ULN's with bounded node outputs are adjusted for maximum Lyapunov component to approach the target value. And, it has been shown that a fully connected ULN with three sigmoidal function nodes is able to generate and eliminate chaotic behaviors by adjusting the parameters.

Original languageEnglish
Pages (from-to)95-104
Number of pages10
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Issue number1
Publication statusPublished - 2000

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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