H∞ state estimation of stochastic neural networks with time-varying delay

The delay-dependent H∞ state estimation problems for stochastic neural networks is considered in this paper. By constructing a suitable Lyapunov-Krasovskii functional, a delay-dependent condition is established to guarantee the estimation error systems to be asymptotical mean-square stable and achieve a prescribed H∞ performance index. Both delay-dependent and delay-independent sufficient conditions for the existence of desired state estimators are derived in terms of linear matrix inequalities(LMIs). Finally, a numerical example demonstrate that the proposed approaches are effective.

[1]  Huijun Gao,et al.  State estimation for discrete-time neural networks with time-varying delays , 2008, Neurocomputing.

[2]  Yongsheng Ding,et al.  Robust state estimation for discrete-time stochastic genetic regulatory networks with probabilistic measurement delays , 2013, Neurocomputing.

[3]  Sabri Arik,et al.  An analysis of exponential stability of delayed neural networks with time varying delays , 2004, Neural Networks.

[4]  Qing-Guo Wang,et al.  Delay-Dependent State Estimation for Delayed Neural Networks , 2006, IEEE Transactions on Neural Networks.

[5]  Chien-Yu Lu,et al.  A Delay-Range-Dependent Approach to Design State Estimator for Discrete-Time Recurrent Neural Networks With Interval Time-Varying Delay , 2008, IEEE Transactions on Circuits and Systems II: Express Briefs.

[6]  Paolo Crippa,et al.  Artificial neural networks as approximators of stochastic processes , 1998, Neural Networks.

[7]  Paolo Crippa,et al.  Artificial Neural Networks as Approximators of Stochastic Processes , 1998 .

[8]  Jinde Cao,et al.  Stability in Cohen–Grossberg-type bidirectional associative memory neural networks with time-varying delays , 2006 .

[9]  Wei Xing Zheng,et al.  Stochastic state estimation for neural networks with distributed delays and Markovian jump , 2012, Neural Networks.

[10]  Jinde Cao,et al.  Guaranteed performance state estimation of static neural networks with time-varying delay , 2011, Neurocomputing.

[11]  Yurong Liu,et al.  State estimation for jumping recurrent neural networks with discrete and distributed delays , 2009, Neural Networks.

[12]  Tingwen Huang,et al.  Brief Papers Further Result on Guaranteed H∞ Performance State Estimation of Delayed Static Neural Networks , 2015 .

[13]  Tingwen Huang,et al.  A mode-dependent approach to state estimation of recurrent neural networks with Markovian jumping parameters and mixed delays , 2013, Neural Networks.

[14]  Rathinasamy Sakthivel,et al.  State estimation for switched discrete-time stochastic BAM neural networks with time varying delay , 2013 .

[15]  Jinde Cao,et al.  Delay-distribution-dependent state estimation for discrete-time stochastic neural networks with random delay , 2011, Neural Networks.

[16]  E. Yaz.  Linear Matrix Inequalities In System And Control Theory , 1998, Proceedings of the IEEE.

[17]  Jinde Cao,et al.  Robust State Estimation for Uncertain Neural Networks With Time-Varying Delay , 2008, IEEE Transactions on Neural Networks.

[18]  Andrzej Cichocki,et al.  Neural networks for optimization and signal processing , 1993 .

[19]  Yurong Liu,et al.  Robust state estimation for discrete-time stochastic neural networks with probabilistic measurement delays , 2010, Neurocomputing.

[20]  Hongye Su,et al.  H∞ state estimation of static neural networks with time-varying delay , 2012, Neurocomputing.

[21]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[22]  Peng Shi,et al.  A delay decomposition approach to L2-Linfinity filter design for stochastic systems with time-varying delay , 2011, Autom..

[23]  Daniel W. C. Ho,et al.  State estimation for delayed neural networks , 2005, IEEE Transactions on Neural Networks.

[24]  Gang Feng,et al.  Delay-Dependent $H_{\infty}$ and Generalized $H_{2}$ Filtering for Delayed Neural Networks , 2009, IEEE Transactions on Circuits and Systems I: Regular Papers.