EQUILIBRIUM AND STABILITY ANALYSIS OF TAKAGI-SUGENO FUZZY DELAYED COHEN-GROSSBERG NEURAL NETWORKS

Ozcan N.

COMMUNICATIONS FACULTY OF SCIENCES UNIVERSITY OF ANKARA-SERIES A1 MATHEMATICS AND STATISTICS, vol.68, no.2, pp.1411-1426, 2019 (ESCI, TRDizin) identifier

  • Publication Type: Article / Article
  • Volume: 68 Issue: 2
  • Publication Date: 2019
  • Doi Number: 10.31801/cfsuasmas.455799
  • Journal Name: COMMUNICATIONS FACULTY OF SCIENCES UNIVERSITY OF ANKARA-SERIES A1 MATHEMATICS AND STATISTICS
  • Journal Indexes: Emerging Sources Citation Index (ESCI), TR DİZİN (ULAKBİM)
  • Page Numbers: pp.1411-1426
  • Keywords: Stability theory, equilibrium analysis, Cohen-Grossberg neural net-works, delayed T-S fuzzy systems, ROBUST EXPONENTIAL STABILITY, ACTIVATION FUNCTIONS, TIME-DELAYS, SYNCHRONIZATION, MULTISTABILITY, SYSTEM
  • Bursa Uludag University Affiliated: Yes

Abstract

This paper carries out an investigation into the problem of the global asymptotic stability of the class of Takagi-Sugeno (T-S) fuzzy delayed Cohen-Grossberg neural networks involving discrete time delays and employing the nondecreasing and slope-bounded activation functions. A new sufficient criterion for the uniqueness and global asymptotic stability of the equilibrium point for this class of fuzzy neural networks is proposed. The uniqueness of the equilibrium point is proved by using the contradiction method, and the stability of the equilibrium point is established by utilizing a novel fuzzy type Lyapunov functional. The obtained stability condition is independent of the time delay parameters and, it can be easily verified by exploiting some commonly used norm properties of matrices. A constructive numerical example is also given to demonstrate the applicability of the proposed stability condition.