Robust stability for uncertain delayed fuzzy Hopfield neural networks with Markovian jumping parameters.
This paper is concerned with the problem of the robust stability of nonlinear delayed Hopfield neural networks (HNNs) with Markovian jumping parameters by Takagi-Sugeno (T-S) fuzzy model. The nonlinear delayed HNNs are first established as a modified T-S fuzzy model in which the consequent parts are composed of a set of Markovian jumping HNNs with interval delays. Time delays here are assumed to be time-varying and belong to the given intervals. Based on Lyapunov-Krasovskii stability theory and linear matrix inequality approach, stability conditions are proposed in terms of the upper and lower bounds of the delays. Finally, numerical examples are used to illustrate the effectiveness of the proposed method.
Li
Hongyi
H
Institute of Complexity Science, Qingdao University, Qingdao 266071, China. lihongyizhq@yahoo.com.cn
Chen
Bing
B
Zhou
Qi
Q
Qian
Weiyi
W
D000465:Algorithms; D017143:Fuzzy Logic; D008390:Markov Chains; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013997:Time Factors