Robust Stability Criterion for Stochastic Recurrent Neural Networks with Markovian Jumping Parameters, Mode-dependent Delays and Multiplicative Noise

摘要：

In this paper, the problem for recurrent neural networks is considered. It is stochastic and contains jumping parameters which are continuous-time Markov process. Delay is mode-dependent and this model is affected by multiplicative noise. Based on the Lyapunov stability theory combined with linear matrix inequalities (LMIs) techniques, we would get some new criteria to guarantee that they are robust stable and their 2 L gains are less than. γ > 0 Introducing into some free weighting matrices would lead to much less conservative results. At last, one numerical example is given to illustrate the effectiveness of the proposed method.

作者: Ji-qing Qiu Hai-kuo He Zhi-feng Gao

作者单位: College of Sciences,Hebei University of Science and Technology,Shijiazhuang 050018,China College of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing Jiangsu

会议类型: 国际会议

会议名称: The 2nd International Symposium on Systems and Control in Aeronautics and Astronautics(第二届航空航天系统与控制国际会议 ISSCAA 2008)

会议地点: 深圳

会议语种:英文

页码: 543-547

在线出版日期: 2008-12-10（万方平台首次上网日期，不代表论文的发表时间）