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 lin...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.展开更多
研究线性终端状态约束下不定随机线性二次最优控制问题．首先利用Lagrange mul tiplier定理得到了存在最优线性状态反馈解的必要条件,而在加强的条件下也得到了最优控制存在的充分条件．从某种意义上讲,以往关于无约束随机线性二次最优...研究线性终端状态约束下不定随机线性二次最优控制问题．首先利用Lagrange mul tiplier定理得到了存在最优线性状态反馈解的必要条件,而在加强的条件下也得到了最优控制存在的充分条件．从某种意义上讲,以往关于无约束随机线性二次最优控制的一些结果可以看成本文主要定理的推论．展开更多
基金Projects(60874030,60835001,60574006)supported by the National Natural Science Foundation of ChinaProjects(07KJB510125,08KJD510008)supported by the Natural Science Foundation of Jiangsu Higher Education Institutions of ChinaProject supported by the Qing Lan Program,Jiangsu Province,China
文摘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.
