Further Results on Passivity Analysis of Neural Networks With Time-Varying Delay
This paper deals with the problem of passivity analysis for neural networks with both time-varying delay and norm-bounded parameter uncertainties. By further utilizing the information of activation function and employing a reciprocally convex approach to consider the relationship between the time-varying delay and its time-varying interval, some improved delay-dependent passivity conditions are obtained, which are formulated in terms of linear matrix inequalities (LMIs) and can be readily solved by existing convex optimization algorithms. Finally, a numerical example is provided to verify the effectiveness of the proposed techniques.
Neural networks passivity delay-dependent Lyapunov-Krasovskii functional linear matrix inequalities (LMIs)
Hong-Bing Zeng Shen-Ping Xiao Chang-Fan Zhang Gang Chen
School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China
国际会议
长沙
英文
161-165
2014-05-31（万方平台首次上网日期，不代表论文的发表时间）