Mixed H∞ and passivity based state estimation for fuzzy neural networks with Markovian-type estimator gain change

This paper is concerned with the mixed H"~ and passivity based state estimation for a class of discrete-time fuzzy neural networks with the estimator gain change, where a discrete-time homogeneous Markov chain taking value in a finite set @C={0,1} is introduced to model this phenomenon. Based on the Markovian system approach and linear matrix inequality technique, a new sufficient condition has been derived such that the estimation error system is exponentially stable in the mean square sense and achieves a prescribed mixed H"~ and passivity performance level. The estimator parameter is then determined by solving a set of linear matrix inequalities (LMIs). A numerical example is presented to show the effectiveness of the proposed design method.

[1]  Magdi S. Mahmoud,et al.  New results for global exponential stability of neural networks with varying delays , 2012, Neurocomputing.

[2]  Xia Huang,et al.  Global exponential estimates for uncertain Markovian jump neural networks with reaction-diffusion terms , 2012 .

[3]  Peng Shi,et al.  Asynchronous I2-I∞ filtering for discrete-time stochastic Markov jump systems with randomly occurred sensor nonlinearities , 2014, Autom..

[4]  Fei Liu,et al.  Finite-time boundedness of uncertain time-delayed neural network with Markovian jumping parameters , 2013, Neurocomputing.

[5]  Stephen P. Boyd,et al.  Linear Matrix Inequalities in Systems and Control Theory , 1994 .

[6]  Shengyuan Xu,et al.  Passivity-based control for uncertain stochastic jumping systems with mode-dependent round-trip time delays , 2012, J. Frankl. Inst..

[7]  Huaguang Zhang,et al.  Novel stability criterions of a new fuzzy cellular neural networks with time-varying delays , 2009, Neurocomputing.

[8]  Xin-Ping Guan,et al.  Delay-dependent guaranteed cost control for T-S fuzzy systems with time delays , 2004, IEEE Transactions on Fuzzy Systems.

[9]  G. Feng,et al.  A Survey on Analysis and Design of Model-Based Fuzzy Control Systems , 2006, IEEE Transactions on Fuzzy Systems.

[10]  Javad Mohammadpour,et al.  Dissipative analysis and control of state-space symmetric systems , 2008, 2008 American Control Conference.

[11]  Ta-lun Yang,et al.  The global stability of fuzzy cellular neural network , 1996 .

[12]  Xiao-Heng Chang,et al.  Robust Nonfragile $H_\infty$ Filtering of Fuzzy Systems With Linear Fractional Parametric Uncertainties , 2012, IEEE Transactions on Fuzzy Systems.

[13]  T. Su,et al.  Delay-dependent stability analysis for recurrent neural networks with time-varying delay , 2008 .

[14]  Fei Liu,et al.  L2-L∞ fuzzy control for Markov jump systems with neutral time-delays , 2013, Math. Comput. Simul..

[15]  Jun Cheng,et al.  Finite-time boundedness of state estimation for neural networks with time-varying delays , 2014, Neurocomputing.

[16]  Chen An,et al.  The Global Stability of Fuzzy Cellular Neural Networks , 2001 .

[17]  Gang Feng,et al.  State estimation of recurrent neural networks with time-varying delay: A novel delay partition approach , 2011, Neurocomputing.

[18]  Daniel W. C. Ho,et al.  Robust H∞ control for a class of nonlinear discrete time-delay stochastic systems with missing measurements , 2009, Autom..

[19]  Guang-Hong Yang,et al.  Non-fragile Hinfinity filter design for linear continuous-time systems , 2008, Autom..

[20]  Ya-Jun Pan,et al.  Output feedback stabilization for Markov-based nonuniformly sampled-data networked control systems , 2013, Syst. Control. Lett..

[21]  Peng Shi,et al.  Stochastic Synchronization of Markovian Jump Neural Networks With Time-Varying Delay Using Sampled Data , 2013, IEEE Transactions on Cybernetics.

[22]  Tong Heng Lee,et al.  Delay-dependent LMI conditions for stability and stabilization of T-S fuzzy systems with bounded time-delay , 2006, Fuzzy Sets Syst..

[23]  Dan Zhang,et al.  Estimator Design for Discrete-Time Switched Neural Networks With Asynchronous Switching and Time-Varying Delay , 2012, IEEE Transactions on Neural Networks and Learning Systems.

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

[25]  Li Yu,et al.  Passivity analysis for discrete-time switched neural networks with various activation functions and mixed time delays , 2011, Nonlinear Dynamics.

[26]  Lihua Xie,et al.  The sector bound approach to quantized feedback control , 2005, IEEE Transactions on Automatic Control.

[27]  C. Ahn.  Delay-dependent state estimation for T-S fuzzy delayed Hopfield neural networks , 2010 .

[28]  Fei Liu,et al.  Stochastic finite-time boundedness of Markovian jumping neural network with uncertain transition probabilities , 2011 .

[29]  Dan Zhang,et al.  Exponential state estimation for Markovian jumping neural networks with time-varying discrete and distributed delays , 2012, Neural Networks.

[30]  Peng Shi,et al.  Sampled-Data Fuzzy Control of Chaotic Systems Based on a T–S Fuzzy Model , 2014, IEEE Transactions on Fuzzy Systems.

[31]  Fei Liu,et al.  Finite-Time $H_{\infty}$ Fuzzy Control of Nonlinear Jump Systems With Time Delays Via Dynamic Observer-Based State Feedback , 2012, IEEE Transactions on Fuzzy Systems.

[32]  Guo-fu Zhai,et al.  COMBINATION FORECASTING METHOD FOR STORAGE RELIABILITY PARAMETERS OF AEROSPACE RELAYS BASED ON GREY-ARTIFICIAL NEURAL NETWORKS , 2013 .

[33]  Qing-Guo Wang,et al.  Fuzzy-Model-Based Fault Detection for a Class of Nonlinear Systems With Networked Measurements , 2013, IEEE Transactions on Instrumentation and Measurement.

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

[35]  Leon O. Chua,et al.  Fuzzy cellular neural networks: theory , 1996, 1996 Fourth IEEE International Workshop on Cellular Neural Networks and their Applications Proceedings (CNNA-96).

[36]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[37]  Peng Shi,et al.  Passivity Analysis for Discrete-Time Stochastic Markovian Jump Neural Networks With Mixed Time Delays , 2011, IEEE Transactions on Neural Networks.

[38]  Zheng-Guang Wu,et al.  Reliable $H_\infty$ Control for Discrete-Time Fuzzy Systems With Infinite-Distributed Delay , 2009, IEEE Transactions on Fuzzy Systems.

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

[40]  Honghai Liu,et al.  Adaptive Sliding-Mode Control for Nonlinear Active Suspension Vehicle Systems Using T–S Fuzzy Approach , 2013, IEEE Transactions on Industrial Electronics.

[41]  Bing Chen,et al.  Robust Stability for Uncertain Delayed Fuzzy Hopfield Neural Networks With Markovian Jumping Parameters , 2009, IEEE Trans. Syst. Man Cybern. Part B.

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