Improved results on H ∞ $\mathcal{H}_{\infty}$ state estimation of static neural networks with interval time-varying delay

Journal of Inequalities and Applications

Xinge Liu¹  Yanjun Shu¹ 

[1] School of Mathematics and Statistics, Central South University;

关键词: static neural networks;    H ∞ $\mathcal{H_{\infty}}$ state estimation;    interval time-varying delay;    globally asymptotically stable;   

DOI  :  10.1186/s13660-016-0990-7

来源: DOAJ

【 摘 要 】

Abstract This paper is concerned with the problem of the guaranteed H ∞ $\mathcal{H_{\infty}}$ performance state estimation for static neural networks with interval time-varying delay. Based on a modified Lyapunov-Krasovskii functional and the linear matrix inequality technique, a novel delay-dependent criterion is presented such that the error system is globally asymptotically stable with guaranteed H ∞ $\mathcal{H_{\infty}}$ performance. In order to obtain less conservative results, Wirtinger’s integral inequality and reciprocally convex approach are employed. The estimator gain matrix can be achieved by solving the LMIs. Numerical examples are provided to demonstrate the effectiveness of the proposed method.

【 授权许可】

Unknown