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} filtering for uncertain discrete-time stochastic neural networks with Markovian jump and mixed time-delays

In this paper, 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} filtering problem is discussed for a class of uncertain discrete-time stochastic neural networks with Markovian jumping parameters and mixed time-delays. Norm-bounded parameter uncertainties exist in both the state and measurement equation. The neuron activation function satisfies sector-bounded condition. The aim is to design a full-order filter with 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. Delay-segment-dependent conditions are developed in terms of linear matrix inequalities (LMIs) such that the resulted filtering error systems robustly stochastically stable. Finally, example is provided to demonstrate the effectiveness and applicability of the related results are obtained in this paper.