Chin. Phys. B ›› 2012, Vol. 21 ›› Issue (12): 120701-120701.doi: 10.1088/1674-1056/21/12/120701
王申全a, 冯健a, 赵青b
Wang Shen-Quan (王申全)a, Feng Jian (冯健)a, Zhao Qing (赵青)b
摘要: In this paper, the problem of delay-distribution-dependent stability is investigated for continuous-time recurrent neural networks (CRNNs) with stochastic delay. Different from the common assumptions on time delays, it is assumed that the probability distribution of the delay taking values in some intervals is known a priori. By making full use of the information concerning the probability distribution of the delay and by using a tighter bounding technique (reciprocally convex combination method), less conservative asymptotic mean-square stable sufficient conditions are derived in terms of linear matrix inequalities (LMIs). Two numerical examples show that our results are better than the existing ones.
中图分类号: (Neural networks, fuzzy logic, artificial intelligence)