Design of delay-dependent exponential estimator for T-S fuzzy neural networks with mixed time-varying interval delays using hybrid Taguchi-genetic algorithm

Kuan Hsuan Tseng, Jason Sheng Hong Tsai, Chien Yu Lu

Research output: Contribution to journalArticle

9 Citations (Scopus)


This paper considers the design of state estimator for Takagi-Sugeno (T-S) fuzzy neural networks with mixed time-varying interval delays. The mixed time-delays that consist of both the discrete time-varying and distributed time-delays with a given range are presented. The activation functions are assumed to be globally Lipschitz continuous. By using the Lyapunov-Krasovskii method, a linear matrix inequality (LMI) approach is developed to construct sufficient conditions for the existence of admissible state estimator such that the error-state system is exponentially globally stable. To avoid complex mathematical derivations and conservative results, a new hybrid Taguchi-genetic algorithm method is integrated with a LMI method to seek the estimator gains that satisfy the Lyapunov-Krasovskii functional stability inequalities. The proposed new approach is straightforward and well adapted to the computer implementation. Therefore, the computational complexity can be reduced remarkably and facilitate the design task of the estimator for T-S fuzzy neural networks with time-varying interval delays. Two illustrative examples are exploited in order to illustrate the effectiveness of the proposed state estimator.

Original languageEnglish
Pages (from-to)49-67
Number of pages19
JournalNeural Processing Letters
Issue number1
Publication statusPublished - 2012 Aug 1


All Science Journal Classification (ASJC) codes

  • Software
  • Neuroscience(all)
  • Computer Networks and Communications
  • Artificial Intelligence

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