Robust stability and H∞\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$H_{\infty}$$\end{document} filter design for neutral stochastic neural networks with parameter uncertainties and time-varying delay

This paper is concerned with the problem of robust stability and H∞\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$H_{\infty}$$\end{document} filter design for neutral stochastic neural networks with parameter uncertainties and time-varying delay. The parameter uncertainties are assumed to be norm-bounded. With the Lyapunov-krasovskii theory, several delay-dependent stability conditions are obtained in terms of liner matrix inequalities(LMIs). Based on the obtained stability criteria, some sufficient conditions for the existence of the robust H∞\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$H_{\infty}$$\end{document} filter are derived. The obtained results ensure the robust stability and a prescribed H∞\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$H_{\infty}$$\end{document} performance level of the filtering error systems for all admissible uncertainties. Finally, two numerical examples are given. One is provided to demonstrate the stability analysis results in this paper are less conservative than some existing results. The other is provided to illustrate the effectiveness of the filter design method.