Finite-time nonfragile time-varying proportional retarded synchronization for Markovian Inertial Memristive NNs with reaction-diffusion items

The issue of synchronization for a class of inertial memristive neural networks over a finite-time interval is investigated in this paper. Specifically, reaction-diffusion items and Markovian jump parameters are both considered in the system model, meanwhile, a novel nonfragile time-varying proportional retarded control strategy is proposed. First, a befitting variable substitution is invoked to transform the original second-order differential system into a first-order one so that the corresponding synchronization error system that is represented by a first-order differential form is established. Second, by utilizing the integral inequality technique, reciprocally convex combination approach and free-weighting matrix method, a less conservative synchronization criterion in terms of linear matrix inequalities is obtained. Finally, three simulations are exploited to illustrate the feasibility, practicability and superiority of the designed controller so that the acquired theoretical results are supported.

[1]  Jinde Cao,et al.  Global dissipativity of memristor-based neutral type inertial neural networks , 2017, Neural Networks.

[2]  Xiaotong Li,et al.  Some new results on stability and synchronization for delayed inertial neural networks based on non-reduced order method , 2017, Neural Networks.

[3]  Jinde Cao,et al.  Matrix measure based dissipativity analysis for inertial delayed uncertain neural networks , 2016, Neural Networks.

[4]  Ju H. Park,et al.  Further Results on Stabilization of Chaotic Systems Based on Fuzzy Memory Sampled-Data Control , 2018, IEEE Transactions on Fuzzy Systems.

[5]  Xiaona Song,et al.  Intermittent pinning synchronization of reaction–diffusion neural networks with multiple spatial diffusion couplings , 2019, Neural Computing and Applications.

[6]  Zhigang Zeng,et al.  New Criteria on Global Stabilization of Delayed Memristive Neural Networks With Inertial Item , 2020, IEEE Transactions on Cybernetics.

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

[8]  Zhigang Zeng,et al.  Passivity and Passification of Fuzzy Memristive Inertial Neural Networks on Time Scales , 2018, IEEE Transactions on Fuzzy Systems.

[9]  Shouming Zhong,et al.  A New Approach to Stabilization of Chaotic Systems With Nonfragile Fuzzy Proportional Retarded Sampled-Data Control , 2019, IEEE Transactions on Cybernetics.

[10]  Huaguang Zhang,et al.  H∞ state estimation for memristive neural networks with time-varying delays: The discrete-time case , 2016, Neural Networks.

[11]  Shengyuan Xu,et al.  Delay-Dependent Stability Criteria for Reaction–Diffusion Neural Networks With Time-Varying Delays , 2013, IEEE Transactions on Cybernetics.

[12]  Ju H. Park,et al.  Non-fragile H∞ synchronization of memristor-based neural networks using passivity theory , 2016, Neural Networks.

[13]  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).

[14]  Zhengqiu Zhang,et al.  Global exponential stability via inequality technique for inertial BAM neural networks with time delays , 2015, Neurocomputing.

[15]  Yan-Wu Wang,et al.  Impulsive Multisynchronization of Coupled Multistable Neural Networks With Time-Varying Delay , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[16]  Fuad E. Alsaadi,et al.  Finite-time event-triggered non-fragile control and fault detection for switched networked systems with random packet losses , 2020, J. Frankl. Inst..

[17]  Zhigang Zeng,et al.  Exponential Adaptive Lag Synchronization of Memristive Neural Networks via Fuzzy Method and Applications in Pseudorandom Number Generators , 2014, IEEE Transactions on Fuzzy Systems.

[18]  Peng Wan,et al.  Passivity analysis of memristor-based impulsive inertial neural networks with time-varying delays. , 2018, ISA transactions.

[19]  Hamid Reza Karimi,et al.  A sliding mode approach to H∞ synchronization of master-slave time-delay systems with Markovian jumping parameters and nonlinear uncertainties , 2012, J. Frankl. Inst..

[20]  Jun Wang,et al.  Robust Synchronization of Multiple Memristive Neural Networks With Uncertain Parameters via Nonlinear Coupling , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[21]  Yonggui Kao,et al.  Global stability of coupled Markovian switching reaction–diffusion systems on networks☆ , 2014 .

[22]  Jigui Jian,et al.  Finite-time synchronization of inertial memristive neural networks with time-varying delays via sampled-date control , 2017, Neurocomputing.

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

[24]  Dan Zhang,et al.  Asynchronous and Resilient Filtering for Markovian Jump Neural Networks Subject to Extended Dissipativity , 2019, IEEE Transactions on Cybernetics.

[25]  Tingwen Huang,et al.  Finite-time synchronization of inertial memristive neural networks with time delay via delay-dependent control , 2018, Neurocomputing.

[26]  Jinde Cao,et al.  Hidden Markov Model-Based Nonfragile State Estimation of Switched Neural Network With Probabilistic Quantized Outputs , 2020, IEEE Transactions on Cybernetics.

[27]  L. Chua.  Memristor-The missing circuit element , 1971 .

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

[29]  Hamid Reza Karimi,et al.  Exponential Stability, Passivity, and Dissipativity Analysis of Generalized Neural Networks With Mixed Time-Varying Delays , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[30]  Jinde Cao,et al.  Stability and synchronization analysis of inertial memristive neural networks with time delays , 2016, Cognitive Neurodynamics.

[31]  Peng Shi,et al.  Passivity-Based Asynchronous Control for Markov Jump Systems , 2017, IEEE Transactions on Automatic Control.

[32]  Jinde Cao,et al.  Switching event-triggered control for global stabilization of delayed memristive neural networks: An exponential attenuation scheme , 2019, Neural Networks.

[33]  Ju H. Park,et al.  Pinning sampled-data synchronization of coupled inertial neural networks with reaction-diffusion terms and time-varying delays , 2017, Neurocomputing.

[34]  Jinde Cao,et al.  Synchronization of discrete-time neural networks with delays and Markov jump topologies based on tracker information , 2017, Neural Networks.

[35]  Zhigang Zeng,et al.  Noise cancellation of memristive neural networks , 2014, Neural Networks.

[36]  Zhigang Zeng,et al.  New results on global exponential dissipativity analysis of memristive inertial neural networks with distributed time-varying delays , 2018, Neural Networks.

[37]  Jinde Cao,et al.  Nonfragile Dissipative Synchronization for Markovian Memristive Neural Networks: A Gain-Scheduled Control Scheme , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[38]  Ruoxia Li,et al.  Synchronization of delayed Markovian jump memristive neural networks with reaction–diffusion terms via sampled data control , 2016, Int. J. Mach. Learn. Cybern..

[39]  Huai-Ning Wu,et al.  Synchronization and Adaptive Control of an Array of Linearly Coupled Reaction-Diffusion Neural Networks With Hybrid Coupling , 2014, IEEE Transactions on Cybernetics.

[40]  Frédéric Gouaisbaut,et al.  Wirtinger-based integral inequality: Application to time-delay systems , 2013, Autom..

[41]  Zhigang Zeng,et al.  Global stabilization analysis of inertial memristive recurrent neural networks with discrete and distributed delays , 2018, Neural Networks.

[42]  Lixiang Li,et al.  Fixed-time synchronization of inertial memristor-based neural networks with discrete delay , 2019, Neural Networks.

[43]  Wei Zhang,et al.  Stochastic exponential synchronization of memristive neural networks with time-varying delays via quantized control , 2018, Neural Networks.

[44]  Junguo Lu.  Global exponential stability and periodicity of reaction–diffusion delayed recurrent neural networks with Dirichlet boundary conditions , 2008 .

[45]  L. Rubchinsky,et al.  Short desynchronization episodes prevail in synchronous dynamics of human brain rhythms. , 2013, Chaos.

[46]  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.

[47]  Zhigang Zeng,et al.  Synchronization of Coupled Reaction–Diffusion Neural Networks With Directed Topology via an Adaptive Approach , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[48]  Xiaona Song,et al.  Quantized output feedback control for nonlinear Markovian jump distributed parameter systems with unreliable communication links , 2019, Appl. Math. Comput..

[49]  Jinde Cao,et al.  Fixed-time synchronization of quaternion-valued memristive neural networks with time delays , 2019, Neural Networks.

[50]  Hao Shen,et al.  Recent Advances in Control and Filtering of Dynamic Systems with Constrained Signals , 2018, Studies in Systems, Decision and Control.