Synchronization of memristive neural networks with leakage delay and parameters mismatch via event-triggered control

In this paper, we investigate the synchronization problem on delayed memristive neural networks (MNNs) with leakage delay and parameters mismatch via event-triggered control. We divide MNNs with parameters mismatch into two categories for discussion. One is state-dependent and can achieve synchronization by designing a suitable controller. A novel Lyapunov functional is constructed to analyze the synchronization problem. Moreover, the triggering conditions are independent from the delay boundaries and can be static or dynamic. Another category of parameters mismatch is structure-dependent and can only achieve quasi-synchronization by appropriate controller. By using matrix measure method and generalized Halanay inequality, a quasi-synchronization criterion is established. The controllers in this paper are discrete state-dependent and can be updated under the event-based triggering condition, which is more simpler than the previous results. In the end of our paper, two illustrative examples are given to support our results.

[1]  Carroll,et al.  Synchronization in chaotic systems. , 1990, Physical review letters.

[2]  Shiping Wen,et al.  Passivity and passification of memristive neural networks with leakage term and time-varying delays , 2019, Appl. Math. Comput..

[3]  Zhigang Zeng,et al.  Synchronization of Switched Neural Networks With Communication Delays via the Event-Triggered Control , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[4]  Zhigang Zeng,et al.  New results on anti-synchronization of switched neural networks with time-varying delays and lag signals , 2016, Neural Networks.

[5]  Zhigang Zeng,et al.  Event-triggered impulsive control on quasi-synchronization of memristive neural networks with time-varying delays , 2019, Neural Networks.

[6]  Shuai Yang,et al.  Exponential Stability of Fractional-Order Impulsive Control Systems With Applications in Synchronization , 2020, IEEE Transactions on Cybernetics.

[7]  D. Stewart,et al.  The missing memristor found , 2008, Nature.

[8]  Liping Wen,et al.  Generalized Halanay inequalities for dissipativity of Volterra functional differential equations , 2008 .

[9]  Sabri Arik,et al.  A new upper bound for the norm of interval matrices with application to robust stability analysis of delayed neural networks , 2013, Neural Networks.

[10]  Zhigang Zeng,et al.  Fuzzy Control for Uncertain Vehicle Active Suspension Systems via Dynamic Sliding-Mode Approach , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[11]  Song Zhu,et al.  New criteria for stochastic suppression and stabilization of hybrid functional differential systems , 2018 .

[12]  Gang Feng,et al.  Event-Based Impulsive Control of Continuous-Time Dynamic Systems and Its Application to Synchronization of Memristive Neural Networks , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[13]  Tianping Chen,et al.  Quasi-synchronization of linearly coupled dynamical networks with directed structure via decentralized event-triggered diffusions , 2016, 2016 12th IEEE International Conference on Control and Automation (ICCA).

[14]  Yong-Ki Ma,et al.  Reliable anti-synchronization conditions for BAM memristive neural networks with different memductance functions , 2016, Appl. Math. Comput..

[15]  Song Zhu,et al.  Global Anti-Synchronization of Complex-Valued Memristive Neural Networks With Time Delays , 2019, IEEE Transactions on Cybernetics.

[16]  Wei Xing Zheng,et al.  Impulsive Stabilization and Impulsive Synchronization of Discrete-Time Delayed Neural Networks , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[17]  Oh-Min Kwon,et al.  Synchronization of fractional-order complex dynamical network with random coupling delay, actuator faults and saturation , 2018 .

[18]  Zhanshan Wang,et al.  Stochastic synchronization of neutral-type chaotic impulse neural networks with leakage delay and Markovian jumping parameters , 2016, Int. J. Intell. Comput. Cybern..

[19]  Zhigang Zeng,et al.  Memristor-based circuit implementation of pulse-coupled neural network with dynamical threshold generators , 2018, Neurocomputing.

[20]  Zhigang Zeng,et al.  Sparse fully convolutional network for face labeling , 2019, Neurocomputing.

[21]  Zhigang Zeng,et al.  A Flux-Controlled Logarithmic Memristor Model and Emulator , 2018, Circuits Syst. Signal Process..

[22]  Zhigang Zeng,et al.  A modified Elman neural network with a new learning rate scheme , 2018, Neurocomputing.

[23]  Tingwen Huang,et al.  Fixed-time stability of dynamical systems and fixed-time synchronization of coupled discontinuous neural networks , 2017, Neural Networks.

[24]  Zhigang Zeng,et al.  Global Exponential Stability and Synchronization for Discrete-Time Inertial Neural Networks With Time Delays: A Timescale Approach , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[25]  Zhigang Zeng,et al.  Passivity analysis of delayed reaction-diffusion memristor-based neural networks , 2019, Neural Networks.

[26]  Linlin Liu,et al.  Synchronization of memristive BAM neural networks with leakage delay and additive time-varying delay components via sampled-data control , 2017 .

[27]  Zhigang Zeng,et al.  General memristor with applications in multilayer neural networks , 2018, Neural Networks.

[28]  Weiwei Liu,et al.  Multi-Label Image Classification by Feature Attention Network , 2019, IEEE Access.

[29]  Jianwei Xia,et al.  Aperiodically Intermittent Control for Quasi-Synchronization of Delayed Memristive Neural Networks: An Interval Matrix and Matrix Measure Combined Method , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[30]  Haijun Jiang,et al.  Edge-Based Fractional-Order Adaptive Strategies for Synchronization of Fractional-Order Coupled Networks With Reaction–Diffusion Terms , 2020, IEEE Transactions on Cybernetics.

[31]  Jun Wang,et al.  Global Exponential Synchronization of Two Memristor-Based Recurrent Neural Networks With Time Delays via Static or Dynamic Coupling , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[32]  Wei Yang Lu,et al.  Nanoscale memristor device as synapse in neuromorphic systems. , 2010, Nano letters.

[33]  Jinde Cao,et al.  Lag Synchronization of Memristor-Based Coupled Neural Networks via $\omega $ -Measure , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[34]  Ju H. Park,et al.  Impulsive Effects on Quasi-Synchronization of Neural Networks With Parameter Mismatches and Time-Varying Delay , 2018, IEEE Transactions on Neural Networks and Learning Systems.

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

[36]  Zhigang Zeng,et al.  CLU-CNNs: Object detection for medical images , 2019, Neurocomputing.

[37]  Jun Wang,et al.  Global Exponential Synchronization of Multiple Memristive Neural Networks With Time Delay via Nonlinear Coupling , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[38]  Hamid Reza Karimi,et al.  Improved Stability and Stabilization Results for Stochastic Synchronization of Continuous-Time Semi-Markovian Jump Neural Networks With Time-Varying Delay , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[39]  Antoine Girard,et al.  Dynamic Triggering Mechanisms for Event-Triggered Control , 2013, IEEE Transactions on Automatic Control.

[40]  Shiping Wen,et al.  Event-Based Synchronization Control for Memristive Neural Networks With Time-Varying Delay , 2019, IEEE Transactions on Cybernetics.

[41]  Zhigang Zeng,et al.  Design of memristor-based image convolution calculation in convolutional neural network , 2016, Neural Computing and Applications.

[42]  Leon O. Chua Resistance switching memories are memristors , 2011 .

[43]  Shouming Zhong,et al.  New passivity criteria for memristive uncertain neural networks with leakage and time-varying delays. , 2015, ISA transactions.

[44]  Jinde Cao,et al.  Dissipativity and quasi-synchronization for neural networks with discontinuous activations and parameter mismatches , 2011, Neural Networks.

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

[46]  Weiwei Liu,et al.  Generating Realistic Videos From Keyframes With Concatenated GANs , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[47]  Zhigang Zeng,et al.  Aperiodic Sampled-Data Sliding-Mode Control of Fuzzy Systems With Communication Delays Via the Event-Triggered Method , 2016, IEEE Transactions on Fuzzy Systems.

[48]  Yiran Chen,et al.  Memristor-Based Design of Sparse Compact Convolutional Neural Network , 2020, IEEE Transactions on Network Science and Engineering.

[49]  Yin Yang,et al.  Memristor-Based Echo State Network With Online Least Mean Square , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[50]  Tingwen Huang,et al.  Synchronization Control for A Class of Discrete Time-Delay Complex Dynamical Networks: A Dynamic Event-Triggered Approach , 2019, IEEE Transactions on Cybernetics.

[51]  Zhigang Zeng,et al.  Adjusting Learning Rate of Memristor-Based Multilayer Neural Networks via Fuzzy Method , 2019, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[52]  K. Gopalsamy Leakage delays in BAM , 2007 .