State estimation of complex-valued neural networks with two additive time-varying delays

Abstract This paper aims at the problem on state estimation of complex-valued neural networks with two additive time-varying delays. Via selecting appropriate Lyapunov–Krasovskii functionals and utilizing reciprocally convex approach and applying matrix inequality technique to analysis, a delay-dependent sufficient condition is derived in the form of linear matrix inequalities (LMIs) to estimate the neuron state with some observed output measurements so as to guarantee the global asymptotic stability of the error-state system. A numerical example is provided to illustrate the feasibility of the obtained result.

[1]  Jinde Cao,et al.  Novel results on stability analysis of neutral-type neural networks with additive time-varying delay components and leakage delay , 2017 .

[2]  Jinde Cao,et al.  Generalized State Estimation for Markovian Coupled Networks Under Round-Robin Protocol and Redundant Channels , 2019, IEEE Transactions on Cybernetics.

[3]  Fuad E. Alsaadi,et al.  Event-based filtering for time-varying nonlinear systems subject to multiple missing measurements with uncertain missing probabilities , 2017, Inf. Fusion.

[4]  Donq-Liang Lee,et al.  Relaxation of the stability condition of the complex-valued neural networks , 2001, IEEE Trans. Neural Networks.

[5]  Jitao Sun,et al.  Further Investigate the Stability of Complex-Valued Recurrent Neural Networks With Time-Delays , 2014 .

[6]  P. Muthukumar,et al.  Global asymptotic stability of complex-valued neural networks with additive time-varying delays , 2017, Cognitive Neurodynamics.

[7]  Daniel W. C. Ho,et al.  Observer-Based Event-Triggering Consensus Control for Multiagent Systems With Lossy Sensors and Cyber-Attacks , 2017, IEEE Transactions on Cybernetics.

[8]  Qiankun Song,et al.  State estimation for neural networks with mixed interval time-varying delays , 2010, Neurocomputing.

[9]  Jun Hu,et al.  A variance-constrained approach to recursive state estimation for time-varying complex networks with missing measurements , 2016, Autom..

[10]  Fuad E. Alsaadi,et al.  Robust ${\mathscr {H}}_{\infty }$ Filtering for a Class of Two-Dimensional Uncertain Fuzzy Systems With Randomly Occurring Mixed Delays , 2017, IEEE Transactions on Fuzzy Systems.

[11]  Yu Zhao,et al.  Asymptotic stability analysis of neural networks with successive time delay components , 2008, Neurocomputing.

[12]  Fuad E. Alsaadi,et al.  A sampled-data approach to distributed H∞ resilient state estimation for a class of nonlinear time-delay systems over sensor networks , 2017, J. Frankl. Inst..

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

[14]  Fuad E. Alsaadi,et al.  Lagrange stability analysis for complex-valued neural networks with leakage delay and mixed time-varying delays , 2017, Neurocomputing.

[15]  Derui Ding,et al.  Distributed recursive filtering for stochastic systems under uniform quantizations and deception attacks through sensor networks , 2017, Autom..

[16]  Tianping Chen,et al.  Global exponential stability of delayed Hopfield neural networks , 2001, Neural Networks.

[17]  PooGyeon Park,et al.  A combined reciprocal convexity approach for stability analysis of static neural networks with interval time-varying delays , 2017, Neurocomputing.

[18]  Qiankun Song,et al.  Global stability of complex-valued neural networks with both leakage time delay and discrete time delay on time scales , 2013, Neurocomputing.

[19]  Fuad E. Alsaadi,et al.  $H_\infty $ Control for 2-D Fuzzy Systems With Interval Time-Varying Delays and Missing Measurements , 2017, IEEE Transactions on Cybernetics.

[20]  Jun Hu,et al.  Event-based state estimation for time-varying stochastic coupling networks with missing measurements under uncertain occurrence probabilities , 2018, Int. J. Gen. Syst..

[21]  Bing Chen,et al.  Global Stability Criterion for Delayed Complex-Valued Recurrent Neural Networks , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[22]  Hao Shen,et al.  Finite-time H∞ asynchronous state estimation for discrete-time fuzzy Markov jump neural networks with uncertain measurements , 2018, Fuzzy Sets Syst..

[23]  Fuad E. Alsaadi,et al.  Dynamics of complex-valued neural networks with variable coefficients and proportional delays , 2018, Neurocomputing.

[24]  Jinde Cao,et al.  Design of extended dissipativity state estimation for generalized neural networks with mixed time-varying delay signals , 2018, Inf. Sci..

[25]  Qiankun Song,et al.  Boundedness and Complete Stability of Complex-Valued Neural Networks With Time Delay , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[26]  Jinde Cao,et al.  Matrix measure method for global exponential stability of complex-valued recurrent neural networks with time-varying delays , 2015, Neural Networks.

[27]  Hong Qiao,et al.  Event-Triggered State Estimation for Discrete-Time Multidelayed Neural Networks With Stochastic Parameters and Incomplete Measurements , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[28]  Huijun Gao,et al.  Stability analysis for continuous systems with two additive time-varying delay components , 2007, Syst. Control. Lett..

[29]  Fuad E. Alsaadi,et al.  Security‐guaranteed filtering for discrete‐time stochastic delayed systems with randomly occurring sensor saturations and deception attacks , 2017 .

[30]  Jeremy S. Smith,et al.  Stability analysis of linear systems with two additive time-varying delays via delay-product-type Lyapunov functional , 2017 .

[31]  Zhenjiang Zhao,et al.  Global exponential stability of impulsive complex-valued neural networks with both asynchronous time-varying and continuously distributed delays , 2016, Neural Networks.

[32]  Tingwen Huang,et al.  Quantized/Saturated Control for Sampled-Data Systems Under Noisy Sampling Intervals: A Confluent Vandermonde Matrix Approach , 2017, IEEE Transactions on Automatic Control.

[33]  Zhenjiang Zhao,et al.  Global exponential stability of complex-valued neural networks with both time-varying delays and impulsive effects , 2016, Neural Networks.

[34]  M. Syed Ali,et al.  Finite-time stability for memristor based switched neural networks with time-varying delays via average dwell time approach , 2018, Neurocomputing.

[35]  Ju H. Park,et al.  An improved stability criterion for generalized neural networks with additive time-varying delays , 2016, Neurocomputing.

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

[37]  Jinde Cao,et al.  Stability analysis of Cohen-Grossberg neural network with both time-varying and continuously distributed delays , 2006 .

[38]  Qing-Long Han,et al.  Consensus control of stochastic multi-agent systems: a survey , 2017, Science China Information Sciences.

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

[40]  Tianping Chen,et al.  Global Exponential Stability for Complex-Valued Recurrent Neural Networks With Asynchronous Time Delays , 2015, IEEE Transactions on Neural Networks and Learning Systems.

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

[42]  Jinde Cao,et al.  Further analysis of global μ-stability of complex-valued neural networks with unbounded time-varying delays , 2015, Neural Networks.

[43]  Zhenjiang Zhao,et al.  Stability analysis of complex-valued neural networks with probabilistic time-varying delays , 2015, Neurocomputing.

[44]  Zhenjiang Zhao,et al.  Impulsive effects on stability of discrete-time complex-valued neural networks with both discrete and distributed time-varying delays , 2015, Neurocomputing.

[45]  Sabri Arik,et al.  An improved robust stability result for uncertain neural networks with multiple time delays , 2014, Neural Networks.

[46]  Jun Hu,et al.  Quantised recursive filtering for a class of nonlinear systems with multiplicative noises and missing measurements , 2013, Int. J. Control.

[47]  Shouming Zhong,et al.  New Delay-Dependent Stability Criteria for Neural Networks With Two Additive Time-varying Delay Components , 2012 .

[48]  Yajuan Liu,et al.  Robust delay-depent stability criteria for uncertain neural networks with two additive time-varying delay components , 2015, Neurocomputing.

[49]  Yingmin Jia,et al.  New approaches on stability criteria for neural networks with two additive time-varying delay components , 2013, Neurocomputing.