Robust stability analysis of Takagi–Sugeno uncertain stochastic fuzzy recurrent neural networks with mixed time-varying delays

doi:10.1088/1674-1056/20/8/080201

Abstract In this paper, the global stability of Takagi—Sugeno (TS) uncertain stochastic fuzzy recurrent neural networks with discrete and distributed time-varying delays (TSUSFRNNs) is considered. A novel LMI-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of TSUSFRNNs. The proposed stability conditions are demonstrated through numerical examples. Furthermore, the supplementary requirement that the time derivative of time-varying delays must be smaller than one is removed. Comparison results are demonstrated to show that the proposed method is more able to guarantee the widest stability region than the other methods available in the existing literature.

Keywords: recurrent neural networks linear matrix inequality Lyapunov stability time-varying delays TS fuzzy model

Received: 05 January 2011
Revised: 05 January 2011
Accepted manuscript online:

PACS:	02.30.Ks	(Delay and functional equations)
	02.30.Sa	(Functional analysis)
	02.60.Cb	(Numerical simulation; solution of equations)
	02.40.Vh	(Global analysis and analysis on manifolds)

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M. Syed Ali Robust stability analysis of Takagi–Sugeno uncertain stochastic fuzzy recurrent neural networks with mixed time-varying delays 2011 Chin. Phys. B 20 080201

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Robust stability analysis of Takagi–Sugeno uncertain stochastic fuzzy recurrent neural networks with mixed time-varying delays

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