Exponential Stability of a Class of Stochastic Interval Cellular Neural Networks

The exponential stability of a class of stochastic interval cellular neural networks with delay is investigated in this paper. For such neural networks, a kind of equivalent description is given ,and several sufficient conditions for the exponential stability in the mean square and surely exponentia...

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Bibliographische Detailangaben
Hauptverfasser:	Jin-fang Han, Ji-qing Qiu
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Artificial intelligence Cellular neural networks Computational intelligence Delay Exponential Stability formula Indium tin oxide Lyapunov function Lyapunov method Neural networks Robust stability Stability criteria Stochastic interval Cellular Neural Networks Stochastic processes Sufficient conditions
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Beschreibung
Zusammenfassung:	The exponential stability of a class of stochastic interval cellular neural networks with delay is investigated in this paper. For such neural networks, a kind of equivalent description is given ,and several sufficient conditions for the exponential stability in the mean square and surely exponential stability are established by the Lyapunov function method and lto formula. The criteria given here are generalizations of some provided in the earlier references.
DOI:	10.1109/AICI.2009.483