A Global Robust Stability Criterion for Jumping Stochastic Cohen- Grossberg Neural Networks with Mode-Dependent Mized Delays
The global robust stability problem is considered for a class of uncertain stochastic Cohen-Grossberg neural networks with Markovian jumping parameters and time-delay in this paper. The time delays are mode-dependent mixed delays including discrete delays and distributed delays. The jumping parameters considered here are generated from a continuous-time discretestate homogenous Markov chain, which are governed by a Markov process with discrete and finite state space. Based on the Lyapunov method and stochastic analysis approaches, a stability criterion is established, which can be expressed in terms of linear matrix inequalities (LMIs). Finally, a numerical example is given to demonstrate the effectiveness of the proposed results.
Cohen-Grossberg Neural Network Markovian Jump Time-Delay Robust Stability Linear Matriz Inequality
Hongjun Chu Lixin Gao
Institute of Operations Research and Control Sciences, Wenzhou University, Zhejiang 325035, China
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
4084-4088
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）