Title :
LMI Approach for Stochastic Stability of Markovian Jumping Hopfield Neural Networks with Wiener Process
Author :
Lou, Xuyang ; Cui, Baotong
Author_Institution :
Res. Center of Control Sci. & Eng., Southern Yangtze Univ., Jiangsu
Abstract :
This paper deals with the stochastic stability problem for Markovian jumping Hopfield neural networks (MJHNNs) with time-varying delays and Wiener process. Our attention is focused on developing sufficient conditions on stochastic stability, even if the system contains Wiener process. All the obtained results are presented in terms of linear matrix inequality. The efficiency of the proposed results is demonstrated via two numerical examples
Keywords :
Hopfield neural nets; Markov processes; linear matrix inequalities; time-varying systems; LMI approach; Markovian jumping Hopfield neural network; Wiener process; linear matrix inequality; stochastic stability; time-varying delay; Asymptotic stability; Delay effects; Hopfield neural networks; Linear matrix inequalities; Neural networks; Stability analysis; Stochastic processes; Stochastic systems; Sufficient conditions; Symmetric matrices;
Conference_Titel :
Intelligent Systems Design and Applications, 2006. ISDA '06. Sixth International Conference on
Conference_Location :
Jinan
Print_ISBN :
0-7695-2528-8
DOI :
10.1109/ISDA.2006.187
