Robust Stability of Switched Uncertain Stochastic Recurrent Neural Networks with Discrete and Distributed Delays
In this paper, some ideas of the switched systems are introduced into the. eld of neural networks and a class of switched uncertain stochastic recurrent neural networks (SUSRNNs) with discrete and distributed delays is investigated. In such neural networks, the features of switched systems, uncertain systems, stochastic systems, as well as time-delay systems are all taken into account. Based on the Lyapunov method and the stochastic analysis approach, some suf.cient conditions are derived by means of linear matrix inequalities (LMIs) to guarantee the SUSRNNs to be globally robustly stable in the mean square. A simulation example is provided to illustrate the effectiveness of the proposed criteria.
Switched systems Recurrent neural networks Norm-bounded uncertainties Stochastic systems Distributed delays
Li Sheng Ming Gao
College of Information and Control Engineering,China University of Petroleum (East China),Dongying 2 College of Information and Electrical Engineering,Shandong University of Science and Technology,Qing
国际会议
2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)
四川绵阳
英文
3884-3889
2011-05-23（万方平台首次上网日期，不代表论文的发表时间）