Event-based sliding-mode synchronization of delayed memristive neural networks via continuous/periodic sampling algorithm

Abstract This paper investigates the problem of event-based sliding-mode synchronization of memristive neural networks with delay through continuous/periodic sampling algorithm. Memristive neural networks are converted into the form of general neural networks by nonsmooth analysis. Then the controller is designed on the sliding surface selected and the trajectory of the system with this controller are analyzed in detail. Based on the continuous sampling, this paper further draws new results with the periodic sampling rule. Finally, some numerical examples are given to verify the correctness of the theoretical results.

[1]  Shiping Wen,et al.  Quantized synchronization of memristive neural networks with time-varying delays via super-twisting algorithm , 2020, Neurocomputing.

[2]  Jing Wang,et al.  Finite-time non-fragile l2-l∞ control for jumping stochastic systems subject to input constraints via an event-triggered mechanism , 2018, J. Frankl. Inst..

[3]  Shiping Wen,et al.  Synchronization of discrete-time recurrent neural networks with time-varying delays via quantized sliding mode control , 2020, Appl. Math. Comput..

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

[5]  Zhigang Zeng,et al.  Lagrange Stability of Memristive Neural Networks With Discrete and Distributed Delays , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[6]  R. Decarlo,et al.  Variable structure control of nonlinear multivariable systems: a tutorial , 1988, Proc. IEEE.

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

[8]  Chuntian Cheng,et al.  Optimizing electrical power production of hydropower system by uniform progressive optimality algorithm based on two-stage search mechanism and uniform design , 2018, Journal of Cleaner Production.

[9]  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.

[10]  W. P. M. H. Heemels,et al.  Periodic Event-Triggered Control for Linear Systems , 2013, IEEE Trans. Autom. Control..

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

[12]  Shihua Li,et al.  A New Second-Order Sliding Mode and Its Application to Nonlinear Constrained Systems , 2019, IEEE Transactions on Automatic Control.

[13]  Daniel W. C. Ho,et al.  Synchronization of Delayed Memristive Neural Networks: Robust Analysis Approach , 2016, IEEE Transactions on Cybernetics.

[14]  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.

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

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

[17]  A. Levant.  Sliding order and sliding accuracy in sliding mode control , 1993 .

[18]  Xiaona Song,et al.  Robust distributed state estimation for Markov coupled neural networks under imperfect measurements , 2020, J. Frankl. Inst..

[19]  G. Bartolini,et al.  Chattering avoidance by second-order sliding mode control , 1998, IEEE Trans. Autom. Control..

[20]  John Y. Hung,et al.  Variable structure control: a survey , 1993, IEEE Trans. Ind. Electron..

[21]  Hao Shen,et al.  Event-triggered passive synchronization for Markov jump neural networks subject to randomly occurring gain variations , 2019, Neurocomputing.

[22]  Dean Zhao,et al.  Finite-time stabilization for a class of high-order stochastic nonlinear systems with an output constraint , 2019, Appl. Math. Comput..

[23]  Yongduan Song,et al.  Event-Triggered Sliding-Mode Control for Multi-Area Power Systems , 2017, IEEE Transactions on Industrial Electronics.

[24]  Chuntian Cheng,et al.  Forecasting reservoir monthly runoff via ensemble empirical mode decomposition and extreme learning machine optimized by an improved gravitational search algorithm , 2019, Appl. Soft Comput..

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

[26]  Xiangxiang Zeng,et al.  Prediction of Potential Disease-Associated MicroRNAs by Using Neural Networks , 2019, Molecular therapy. Nucleic acids.

[27]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Victor Sreeram,et al.  Distributed Dissipative State Estimation for Markov Jump Genetic Regulatory Networks Subject to Round-Robin Scheduling , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[29]  Jinde Cao,et al.  ${H}_{\infty }$ Filtering for Fuzzy Jumping Genetic Regulatory Networks With Round-Robin Protocol: A Hidden-Markov-Model-Based Approach , 2020, IEEE Transactions on Fuzzy Systems.

[30]  Wei Zhang,et al.  Adaptive synchronization of memristive neural networks with time-varying delays and reaction-diffusion term , 2017, Appl. Math. Comput..

[31]  Zhenyuan Guo,et al.  Global synchronization of memristive neural networks subject to random disturbances via distributed pinning control , 2016, Neural Networks.

[32]  Shiping Wen,et al.  Sliding mode control of neural networks via continuous or periodic sampling event-triggering algorithm , 2020, Neural Networks.

[33]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[34]  Jinde Cao,et al.  Exponential Synchronization of Memristive Neural Networks With Delays: Interval Matrix Method , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[35]  Sen Wang,et al.  Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization , 2019, Journal of Hydrology.

[36]  Shiping Wen,et al.  Passivity and passification of memristive recurrent neural networks with multi-proportional delays and impulse , 2020, Appl. Math. Comput..

[37]  Jinde Cao,et al.  Asymptotic synchronization for stochastic memristor-based neural networks with noise disturbance , 2016, J. Frankl. Inst..

[38]  Q. Zou,et al.  Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA , 2018, RNA.

[39]  Shiping Wen,et al.  Multilabel Image Classification via Feature/Label Co-Projection , 2021, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[40]  Jing Wang,et al.  Reachable set estimation for Markov jump LPV systems with time delays , 2020, Appl. Math. Comput..

[41]  Massimiliano Di Ventra,et al.  Experimental demonstration of associative memory with memristive neural networks , 2009, Neural Networks.

[42]  Shiping Wen,et al.  Synchronization of memristive neural networks with leakage delay and parameters mismatch via event-triggered control , 2019, Neural Networks.

[43]  Zhen Wang,et al.  Quantized asynchronous dissipative state estimation of jumping neural networks subject to occurring randomly sensor saturations , 2018, Neurocomputing.

[44]  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..

[45]  Chuntian Cheng,et al.  Forecasting Daily Runoff by Extreme Learning Machine Based on Quantum-Behaved Particle Swarm Optimization , 2018 .

[46]  Xinghuo Yu,et al.  Periodic event-triggered sliding mode control , 2018, Autom..

[47]  Jinde Cao,et al.  Exponential H∞ Filtering for Continuous-Time Switched Neural Networks Under Persistent Dwell-Time Switching Regularity , 2020, IEEE Transactions on Cybernetics.

[48]  Chuntian Cheng,et al.  Optimization of hydropower reservoirs operation balancing generation benefit and ecological requirement with parallel multi-objective genetic algorithm , 2018, Energy.

[49]  Shiping Wen,et al.  Global exponential synchronization of delayed memristive neural networks with reaction-diffusion terms , 2019, Neural Networks.

[50]  Zhong-kai Feng,et al.  Mixed Integer Linear Programming Model for Peak Operation of Gas-Fired Generating Units with Disjoint-Prohibited Operating Zones , 2019 .

[51]  Jing Wang,et al.  Asynchronous dissipative filtering for nonlinear jumping systems subject to fading channels , 2020, J. Frankl. Inst..