Balasubramaniam, P.; Ali, M. Syed Robust exponential stability of uncertain fuzzy Cohen-Grossberg neural networks with time-varying delays. (English) Zbl 1185.68511 Fuzzy Sets Syst. 161, No. 4, 608-618 (2010). Summary: In this paper, the Takagi-Sugeno (T-S) fuzzy model representation is extended to the stability analysis for uncertain Cohen-Grossberg neural networks (CGNNs) with time-varying delays. A novel linear matrix inequality based stability criterion is obtained by using Lyapunov functional theory to guarantee the exponential stability of uncertain CGNNs with time varying delays which are represented by T-S fuzzy models. Finally, the proposed stability conditions are demonstrated with numerical examples. Cited in 18 Documents MSC: 68T05 Learning and adaptive systems in artificial intelligence 93C42 Fuzzy control/observation systems 93D05 Lyapunov and other classical stabilities (Lagrange, Poisson, \(L^p, l^p\), etc.) in control theory Keywords:Cohen-Grossberg neural networks; global exponential stability; linear matrix inequality; Lyapunov functional; time-varying delays; T-S fuzzy model Software:LMI toolbox PDF BibTeX XML Cite \textit{P. Balasubramaniam} and \textit{M. S. Ali}, Fuzzy Sets Syst. 161, No. 4, 608--618 (2010; Zbl 1185.68511) Full Text: DOI References: [1] Arik, S.; Orman, Z., Global stability analysis of Cohen-Grossberg neural networks with time varying delays, Phys. Lett. A, 341, 410-421 (2005) · Zbl 1171.37337 [2] Boyd, B.; Ghoui, L. E.; Feron, E.; Balakrishnan, V., Linear Matrix Inequalities in System and Control Theory (1994), SIAM: SIAM Philadelphia, PA [3] Cao, Y. Y.; Frank, P. 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