摘要
The problem of passivity analysis for a class of discrete-time stochastic neural networks (DSNNs) with time-varying interval delay was investigated. The delay-dependent sufficient criteria were derived in terms of linear matrix inequalities (LMIs). The results are shown to be generalization of some previous results and are less conservative than the existing works. Meanwhile, the computational complexity of the obtained stability conditions is reduced because less variables are involved. A numerical example is given to show the effectiveness and the benefits of the proposed method.
The problem of passivity analysis for a class of discrete-time stochastic neural networks (DSNNs) with time-varying interval delay was investigated. The delay-dependent sufficient criteria were derived in terms of linear matrix inequalities (LMIs). The results are shown to be generalization of some previous results and are less conservative than the existing works. Meanwhile, the computational complexity of the obtained stability conditions is reduced because less variables are involved. A numerical example is given to show the effectiveness and the benefits of the proposed method.
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2009年第S1期63-67,共5页
Journal of Central South University:Science and Technology
基金
Projects(60874030,60835001,60574006)supported by the National Natural Science Foundation of China
Projects(07KJB510125,08KJD510008)supported by the Natural Science Foundation of Jiangsu Higher Education Institutions of China
Project supported by the Qing Lan Program,Jiangsu Province,China
