Protocol-based state estimation for delayed Markovian jumping neural networks

This paper is concerned with the state estimation problem for a class of Markovian jumping neural networks (MJNNs) with sensor nonlinearities, mode-dependent time delays and stochastic disturbances subject to the Round-Robin (RR) scheduling mechanism. The system parameters experience switches among finite modes according to a Markov chain. As an equal allocation scheme, the RR communication protocol is introduced for efficient usage of limited bandwidth and energy saving. The update matrix method is adopted to deal with the periodic time-delays resulting from the RR protocol. The objective of the addressed problem is to construct a state estimator for the MJNNs such that the dynamics of the estimation error is exponentially ultimately bounded in the mean square with a certain upper bound. Sufficient conditions are established for the existence of the desired state estimator by resorting to a combination of the Lyapunov stability theory and the stochastic analysis technique. Furthermore, the estimator gain matrices are characterized in terms of the solution to a convex optimization problem. Finally, a numerical simulation example is exploited to demonstrate the effectiveness of the proposed estimator design strategy.

[1]  Hamid Reza Karimi,et al.  Stability of Markovian Jump Generalized Neural Networks With Interval Time-Varying Delays , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[2]  Zidong Wang,et al.  State estimation for two‐dimensional complex networks with randomly occurring nonlinearities and randomly varying sensor delays , 2014 .

[3]  Rathinasamy Sakthivel,et al.  Combined H∞ and passivity state estimation of memristive neural networks with random gain fluctuations , 2015, Neurocomputing.

[4]  Jinde Cao,et al.  Impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach , 2015, Neural Networks.

[5]  M. Syed Ali,et al.  Decentralized event-triggered synchronization of uncertain Markovian jumping neutral-type neural networks with mixed delays , 2017, Neural Networks.

[6]  Huaguang Zhang,et al.  Novel Weighting-Delay-Based Stability Criteria for Recurrent Neural Networks With Time-Varying Delay , 2010, IEEE Transactions on Neural Networks.

[7]  Huaguang Zhang,et al.  A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[8]  Hongli Dong,et al.  A survey on set-membership filtering for networked control systems under communication protocols , 2018 .

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

[10]  Zidong Wang,et al.  Filtering for a class of nonlinear discrete-time stochastic systems with state delays , 2007 .

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

[12]  Ju H. Park,et al.  State estimation of memristor-based recurrent neural networks with time-varying delays based on passivity theory , 2014, Complex..

[13]  Huijun Gao,et al.  New Passivity Analysis for Neural Networks With Discrete and Distributed Delays , 2010, IEEE Transactions on Neural Networks.

[14]  Qiankun Song,et al.  Design of controller on synchronization of chaotic neural networks with mixed time-varying delays , 2009, Neurocomputing.

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

[16]  Nathan van de Wouw,et al.  Decentralized observer-based control via networked communication , 2013, Autom..

[17]  Jun Hu,et al.  Delay-dependent stability analysis for continuous-time BAM neural networks with Markovian jumping parameters , 2010, Neural Networks.

[18]  Peng Shi,et al.  Exponential Stability on Stochastic Neural Networks With Discrete Interval and Distributed Delays , 2010, IEEE Transactions on Neural Networks.

[19]  Zidong Wang,et al.  Variance-Constrained State Estimation for Complex Networks With Randomly Varying Topologies , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[20]  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.

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

[22]  Huaguang Zhang,et al.  Robust Global Exponential Synchronization of Uncertain Chaotic Delayed Neural Networks via Dual-Stage Impulsive Control , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[23]  Zidong Wang,et al.  Design of exponential state estimators for neural networks with mixed time delays , 2007 .

[24]  Lei Zou,et al.  State Estimation for Discrete-Time Dynamical Networks With Time-Varying Delays and Stochastic Disturbances Under the Round-Robin Protocol , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[25]  Ligang Wu,et al.  State estimation and sliding mode control for semi-Markovian jump systems with mismatched uncertainties , 2015, Autom..

[26]  Min Wu,et al.  Delay-dependent stability analysis of neural networks with time-varying delay: A generalized free-weighting-matrix approach , 2017, Appl. Math. Comput..

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

[28]  Qing-Long Han,et al.  Neuronal State Estimation for Neural Networks With Two Additive Time-Varying Delay Components , 2017, IEEE Transactions on Cybernetics.

[29]  Fuad E. Alsaadi,et al.  A new approach to non-fragile state estimation for continuous neural networks with time-delays , 2016, Neurocomputing.

[30]  R. Rakkiyappan,et al.  Exponential synchronization of Markovian jumping neural networks with partly unknown transition probabilities via stochastic sampled-data control , 2014, Neurocomputing.

[31]  Ju H. Park,et al.  Further results on state estimation for neural networks of neutral-type with time-varying delay , 2009, Appl. Math. Comput..

[32]  Zidong Wang,et al.  H∞ state estimation with fading measurements, randomly varying nonlinearities and probabilistic distributed delays , 2015 .

[33]  Wei Xing Zheng,et al.  Exponential Stability Analysis for Delayed Neural Networks With Switching Parameters: Average Dwell Time Approach , 2010, IEEE Transactions on Neural Networks.

[34]  El-Kébir Boukas,et al.  Control of Singular Systems with Random Abrupt Changes , 2008 .

[35]  Peter Tiño,et al.  Markovian architectural bias of recurrent neural networks , 2004, IEEE Transactions on Neural Networks.

[36]  Zidong Wang,et al.  State estimation for discrete-time Markovian jumping neural networks with mixed mode-dependent delays ☆ , 2008 .

[37]  Huijun Gao,et al.  On H-infinity Estimation of Randomly Occurring Faults for A Class of Nonlinear Time-Varying Systems With Fading Channels , 2016, IEEE Transactions on Automatic Control.

[38]  Zidong Wang,et al.  Event-triggered distributed ℋ ∞ state estimation with packet dropouts through sensor networks , 2015 .

[39]  Fuad E. Alsaadi,et al.  State estimation for a class of artificial neural networks with stochastically corrupted measurements under Round-Robin protocol , 2016, Neural Networks.

[40]  Hamid Reza Karimi,et al.  New Delay-Dependent Exponential $H_{\infty}$ Synchronization for Uncertain Neural Networks With Mixed Time Delays , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

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

[42]  Zidong Wang,et al.  State Estimation for Coupled Uncertain Stochastic Networks With Missing Measurements and Time-Varying Delays: The Discrete-Time Case , 2009, IEEE Transactions on Neural Networks.

[43]  Ju H. Park,et al.  Improved delay-dependent exponential stability for uncertain stochastic neural networks with time-varying delays , 2010 .

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

[45]  James Lam,et al.  Stability and Dissipativity Analysis of Distributed Delay Cellular Neural Networks , 2011, IEEE Transactions on Neural Networks.

[46]  Hongli Dong,et al.  Filter design, fault estimation and reliable control for networked time-varying systems: a survey , 2017 .

[47]  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.

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

[49]  Fuad E. Alsaadi,et al.  H∞ state estimation for discrete-time neural networks with distributed delays and randomly occurring uncertainties through Fading channels , 2017, Neural Networks.

[50]  Liang Chen,et al.  Bio-inspired neural network with application to license plate recognition: hysteretic ELM approach , 2016 .

[51]  Huijun Gao,et al.  Finite-horizon reliable control with randomly occurring uncertainties and nonlinearities subject to output quantization , 2015, Autom..