Event-triggered non-fragile state estimation for delayed neural networks with randomly occurring sensor nonlinearity

Abstract This paper is concerned with event-triggered non-fragile state estimator design for delayed neural networks subject to randomly occurring sensor nonlinearity. Different from the existing event-triggered scheme, a new event-triggered scheme is designed which is dependent on the incomplete measurement. The adopted event-triggered scheme is introduced between the neural networks and state estimator for the purpose of energy saving. Considering the sensor nonlinearity and using the event-triggered scheme, a new estimation error system is modeled. Based on this model, a sufficient condition is derived to guarantee the asymptotical stability of estimation error system. Furthermore, a desired event-triggered non-fragile estimator is designed by solving a set of linear matrix inequalities. Finally, a numerical example is provided to illustrate the usefulness of the proposed method.

[1]  Hao Shen,et al.  Extended Dissipative State Estimation for Markov Jump Neural Networks With Unreliable Links , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[2]  Fuad E. Alsaadi,et al.  Non-fragile state estimation for discrete Markovian jumping neural networks , 2016, Neurocomputing.

[3]  Ligang Wu,et al.  Reliable Filtering With Strict Dissipativity for T-S Fuzzy Time-Delay Systems , 2014, IEEE Transactions on Cybernetics.

[4]  Zidong Wang,et al.  Variance-constrained H∞ control for a class of nonlinear stochastic discrete time-varying systems: The event-triggered design , 2016, Autom..

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

[6]  Dong Yue,et al.  To Transmit or Not to Transmit: A Discrete Event-Triggered Communication Scheme for Networked Takagi–Sugeno Fuzzy Systems , 2013, IEEE Transactions on Fuzzy Systems.

[7]  Peng Shi,et al.  Stochastic finite-time state estimation for discrete time-delay neural networks with Markovian jumps , 2015, Neurocomputing.

[8]  Ligang Wu,et al.  Event-Triggered Fault Detection of Nonlinear Networked Systems , 2017, IEEE Transactions on Cybernetics.

[9]  Ahmed Alsaedi,et al.  Event-triggered multi-rate fusion estimation for uncertain system with stochastic nonlinearities and colored measurement noises , 2017, Inf. Fusion.

[10]  Meiqin Liu,et al.  $H_{\infty }$ State Estimation for Discrete-Time Delayed Systems of the Neural Network Type With Multiple Missing Measurements , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[11]  Yuanqing Xia,et al.  A New Result on $H_{\infty }$ State Estimation of Delayed Static Neural Networks , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[12]  Dan Zhang,et al.  State estimation with guaranteed performance for switching-type fuzzy neural networks in presence of sensor nonlinearities , 2014, Commun. Nonlinear Sci. Numer. Simul..

[13]  Jia Wang,et al.  Event-Triggered Generalized Dissipativity Filtering for Neural Networks With Time-Varying Delays , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[14]  Qing-Guo Wang,et al.  Mode-dependent filter design for Markov jump systems with sensor nonlinearities in finite frequency domain , 2017, Signal Process..

[15]  Laurent El Ghaoui,et al.  Robust Solutions to Least-Squares Problems with Uncertain Data , 1997, SIAM J. Matrix Anal. Appl..

[16]  Dong Yue,et al.  Distributed event-triggered control of discrete-time heterogeneous multi-agent systems , 2013, J. Frankl. Inst..

[17]  Engang Tian,et al.  Co-design of event generator and filtering for a class of T-S fuzzy systems with stochastic sensor faults , 2015, Fuzzy Sets Syst..

[18]  W. P. M. H. Heemels,et al.  Event-triggered control systems under packet losses , 2017, Autom..

[19]  Fuad E. Alsaadi,et al.  Finite-horizon H∞ filtering for switched time-varying stochastic systems with random sensor nonlinearities and packet dropouts , 2017, Signal Process..

[20]  Mou Chen,et al.  Robust adaptive neural network synchronization controller design for a class of time delay uncertain chaotic systems , 2009 .

[21]  Dimos V. Dimarogonas,et al.  Event-triggered intermittent sampling for nonlinear model predictive control , 2017, Autom..

[22]  Renquan Lu,et al.  Distributed state estimation for periodic systems with sensor nonlinearities and successive packet dropouts , 2017, Neurocomputing.

[23]  K. C. Cheung,et al.  Passivity criteria for continuous-time neural networks with mixed time-varying delays , 2012, Appl. Math. Comput..

[24]  Dong Yue,et al.  Event-triggering in networked systems with probabilistic sensor and actuator faults , 2013, Inf. Sci..

[25]  Renquan Lu,et al.  Finite-Time State Estimation for Coupled Markovian Neural Networks With Sensor Nonlinearities , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[26]  Hongye Su,et al.  H∞ filtering for discrete fuzzy stochastic systems with randomly occurred sensor nonlinearities , 2015, Signal Process..

[27]  Chee Seng Chan,et al.  Non-fragile state observer design for neural networks with Markovian jumping parameters and time-delays , 2014 .

[28]  Feng Wang,et al.  H∞ filtering for uncertain systems with time-delay and randomly occurred sensor nonlinearities , 2016, Neurocomputing.

[29]  PooGyeon Park,et al.  Reciprocally convex approach to stability of systems with time-varying delays , 2011, Autom..

[30]  Fuad E. Alsaadi,et al.  Event-triggered distributed state estimation for a class of time-varying systems over sensor networks with redundant channels , 2017, Inf. Fusion.