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Delay-dependent H ∞ and generalized H 2 filtering for stochastic neural networks with time-varying delay and noise disturbance

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Abstract

This paper presents the delay-dependent \(H_\infty\) and generalized H 2 filters design for stochastic neural networks with time-varying delay and noise disturbance. The stochastic neural networks under consideration are subject to time-varying delay in both the state and measurement equations. The aim is to design a stable full-order linear filter assuring asymptotical mean-square stability and a prescribed \(H_\infty\) or generalized H 2 performance indexes for the filtering error systems. Delay-dependent sufficient conditions for the existence of \(H_\infty\) and generalized H 2 filters are both proposed in terms of linear matrix inequalities. Finally, numerical example demonstrates that the proposed approaches are effective.

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Acknowledgments

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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Authors and Affiliations

  1. College of Computer and Information, Hohai University, Changzhou, 213022, China

    Mingang Hua, Huasheng Tan & Junfeng Chen

  2. Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Changzhou, 213022, China

    Mingang Hua

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  1. Mingang Hua
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  2. Huasheng Tan
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Correspondence to Mingang Hua.

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Hua, M., Tan, H. & Chen, J. Delay-dependent H ∞ and generalized H 2 filtering for stochastic neural networks with time-varying delay and noise disturbance. Neural Comput & Applic 25, 613–624 (2014). https://doi.org/10.1007/s00521-013-1531-7

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  • DOI: https://doi.org/10.1007/s00521-013-1531-7

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