NEW STOCHASTIC STABILITY CRITERIA OF HOPFIELD NEURAL NETWORKS WITH MARKOVIAN JUMP PARAMETERS

摘要：

In this paper, the problem of stochastic stability for a class of time-delay Hopfield neural networks with Markovian jump parameters is investigated. The jumping parameters are modeled as a continuous-time, discrete-state Markov process. Without assuming the boundedness, monotonicity and differentiability of the activation functions, the result for delay-dependent stochastic stability criteria for the Markovian jumping Hopfield neural networks (MJDHNNs) with time-delay are developed. We establish that the sufficient conditions can be essentially solved in terms of linear matrix inequalities.

关键词： Hopfield neural Networks Markovian jump parameters Stochastic stability Linear matriz inequality

作者: GUI-JU SHI JI-QING QIU JING CHEN HAI-KUO HE GUO-GANG L

作者单位: College of Graduate, Hebei University of Science and Technology, Shijiazhuang 050018, China College of Sciences, Hebei University of Science and Technology, Shijiazhuang 050018, China ICollege of Sciences, Hebei University of Science and Technology, Shijiazhuang 050018, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 811-814

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）