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MIXED DELAY-DEPENDENT STABILITY OF HIGH-ORDER NEURAL NETWORKS BASED ON A WEAK COUPLING LMI SET

Part of:Time-dependent statistical mechanics (dynamic and nonequilibrium)

Published online by Cambridge University Press:  09 March 2010

MINGHAO LI,
WUNENG ZHOU,
ZIWEI NI and
MINGJUN WANG
MINGHAO LI
Affiliation:
College of Information Science and Technology, Donghua University, Shanghai, PR China (email: wnzhou@dhu.edu.cn)
WUNENG ZHOU*
Affiliation:
College of Information Science and Technology, Donghua University, Shanghai, PR China (email: wnzhou@dhu.edu.cn)
ZIWEI NI
Affiliation:
Department of Computer Science, Xiamen University, Xiamen, PR China (email: zwni@xmu.edu.cn)
MINGJUN WANG
Affiliation:
College of Information Science and Technology, Donghua University, Shanghai, PR China (email: wnzhou@dhu.edu.cn)
*
✉For correspondence; e-mail: wnzhou@dhu.edu.cn
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Abstract

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This paper deals with the problem of discrete and distributed time-delay exponential stability for deterministic and uncertain stochastic high-order neural networks. The concept of a parameter weak coupling linear matrix inequality set (PWCLMIS) is developed. New results are derived in terms of PWCLMIS. Large mixed time delays can be obtained by using this approach. Furthermore, these results are more general than some previous existence results. Two numerical examples are given to show the merit of the approach.

Keywords

neural networksmixed delaysexponential stabilityparameters weak coupling linear matrix inequality set (PWCLMIS)

MSC classification

Secondary:82C32: Neural nets

Information

Type
Research Article
Information
The ANZIAM Journal , Volume 51 , Issue 1 , July 2009, pp. 123 - 140
Copyright
Copyright © Australian Mathematical Society 2010

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