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Title of Journal: Int J Mach Learn Cyber

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Abbravation: International Journal of Machine Learning and Cybernetics

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Springer Berlin Heidelberg

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DOI

10.1016/0304-4165(70)90145-5

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ISSN

1868-808X

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State estimation for uncertain discretetime stoch

Authors: Mingang Hua Huasheng Tan Juntao Fei
Publish Date: 2015/05/10
Volume: 8, Issue: 3, Pages: 823-835
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Abstract

The state estimation problem is considered for a class of discretetime stochastic neural networks with Markovian jumping parameters in this paper Normbounded parameter uncertainties in the state and measurement equation and timevarying delays are investigated The neuron activation function satisfies sectorbounded conditions and the nonlinear perturbation of the measurement equation satisfies standard Lipschitz condition and sectorbounded conditions By constructing proper Lyapunov–Krasovskii functional delaydependent conditions are developed in terms of linear matrix inequalities LMIs to estimate the neuron state such that the dynamic of the estimation error system is asymptotically stable Finally numerical examples are shown to demonstrate the effectiveness and applicability of the proposed design methodThe authors wish to thank the editor and the anonymous reviewers very much for their valuable comments and suggestions which have led to significant improvements of the quality of this manuscript This work was supported by the Natural Science Foundation of Jiangsu Province No BK20130239 and the Research Fund for the Doctoral Program of Higher Education of China No 20130094120015

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