Further result on \(\mathcal{H}_\infty\) state estimation of static neural networks with interval time-varying delay


Authors

Xiaojun Zhang - School of Mathematics Sciences, University of Electronic Science and Technology of China, Chengdu Sichuan 611731, P. R. China. Xin Wang - School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu Sichuan 611731, P. R. China. Shouming Zhong - School of Mathematics Sciences, University of Electronic Science and Technology of China, Chengdu Sichuan 611731, P. R. China.


Abstract

This paper considers the \(\mathcal{H}_\infty\) state estimation problem of static neural networks with interval timevarying delay. By constructing a suitable Lyapunov-Krasovskii functional, the single-integral and doubleintegral terms in the time derivative of the Lyapunov functional are handled by utilizing the inverses of first-order and squared reciprocally convex parameters techniques. An improved delay dependent criterion is established such that the error system is globally asymptotically stable with \(\mathcal{H}_\infty\) performance. The desired estimator gain matrix and the optimal performance index are obtained via solving a convex optimization problem subject to linear matrix inequalities. Two numerical examples are given to illustrate the effectiveness of the proposed method.


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ISRP Style

Xiaojun Zhang, Xin Wang, Shouming Zhong, Further result on \(\mathcal{H}_\infty\) state estimation of static neural networks with interval time-varying delay, Journal of Nonlinear Sciences and Applications, 9 (2016), no. 8, 5291--5305

AMA Style

Zhang Xiaojun, Wang Xin, Zhong Shouming, Further result on \(\mathcal{H}_\infty\) state estimation of static neural networks with interval time-varying delay. J. Nonlinear Sci. Appl. (2016); 9(8):5291--5305

Chicago/Turabian Style

Zhang, Xiaojun, Wang, Xin, Zhong, Shouming. "Further result on \(\mathcal{H}_\infty\) state estimation of static neural networks with interval time-varying delay." Journal of Nonlinear Sciences and Applications, 9, no. 8 (2016): 5291--5305


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