Finite-time Synchronization of Neural Networks with Multiple Proportional Delays via Non-chattering Control

This paper investigates finite-time synchronization of neural networks (NNs) with multiple proportional delays. In order to cope with the difficulties induced by multiple proportional delays, suitable nonlinear variable transformations and new 1-norm-based analytical techniques are developed. By constructing Lyapunov functional and designing new designed controllers, several new sufficient conditions are derived to realize synchronization in finite time. Moreover, estimation of the upper bound of synchronization time is also provided for NNs with bounded delays or proportional delays. The designed controllers without sign function are simple, which means the chattering phenomenon in most of the existing results can be overcome. A numerical simulation is offered to verify the effectiveness of the theoretical analysis.

[1]  Yan-Wu Wang,et al.  Synchronization of Continuous Dynamical Networks With Discrete-Time Communications , 2011, IEEE Transactions on Neural Networks.

[2]  Xinsong Yang,et al.  Finite-Time Synchronization of Coupled Networks With Markovian Topology and Impulsive Effects , 2016, IEEE Transactions on Automatic Control.

[3]  Gang Feng,et al.  Synchronization of nonidentical chaotic neural networks with time delays , 2009, Neural Networks.

[4]  Ju H. Park,et al.  Finite-time synchronization control for uncertain Markov jump neural networks with input constraints , 2014, Nonlinear Dynamics.

[5]  Tao Li,et al.  Exponential synchronization for arrays of coupled neural networks with time-delay couplings , 2011 .

[6]  Kate Smith-Miles,et al.  A unified framework for chaotic neural-network approaches to combinatorial optimization , 1999, IEEE Trans. Neural Networks.

[7]  Ju H. Park,et al.  On stability criteria for neural networks with time-varying delay using Wirtinger-based multiple integral inequality , 2015, J. Frankl. Inst..

[8]  Bingwen Liu,et al.  Global exponential convergence of non-autonomous cellular neural networks with multi-proportional delays , 2016, Neurocomputing.

[9]  Daniel W. C. Ho,et al.  Finite-Time Cluster Synchronization of T–S Fuzzy Complex Networks With Discontinuous Subsystems and Random Coupling Delays , 2015, IEEE Transactions on Fuzzy Systems.

[10]  Chuandong Li,et al.  Delay-dependent exponential stability analysis of bi-directional associative memory neural networks with time delay: an LMI approach , 2005 .

[11]  Ju H. Park,et al.  Synchronization of a complex dynamical network with coupling time-varying delays via sampled-data control , 2012, Appl. Math. Comput..

[12]  Xinsong Yang,et al.  Can neural networks with arbitrary delays be finite-timely synchronized? , 2014, Neurocomputing.

[13]  Ze Tang,et al.  Distributed impulsive synchronization of Lur'e dynamical networks via parameter variation methods , 2018 .

[14]  Frank C. Hoppensteadt,et al.  Pattern recognition via synchronization in phase-locked loop neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..

[15]  Tingwen Huang,et al.  Passivity and Output Synchronization of Complex Dynamical Networks With Fixed and Adaptive Coupling Strength , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[16]  Hao Shen,et al.  Finite-Time Cluster Synchronization of Lur’e Networks: A Nonsmooth Approach , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[17]  Le Van Hien,et al.  Finite-time stability of a class of non-autonomous neural networks with heterogeneous proportional delays , 2015, Appl. Math. Comput..

[18]  Jinde Cao,et al.  Matrix measure based stability criteria for high-order neural networks with proportional delay , 2015, Neurocomputing.

[19]  Liqun Zhou Dissipativity of a class of cellular neural networks with proportional delays , 2013 .

[20]  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.

[21]  Zhidong Teng,et al.  Finite-time synchronization for memristor-based neural networks with time-varying delays , 2015, Neural Networks.

[22]  Chuandong Li,et al.  Finite-Time Synchronization of Discontinuous Neural Networks With Delays and Mismatched Parameters , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[23]  Ju H. Park,et al.  Stability Analysis of Neural Networks With Time-Varying Delay by Constructing Novel Lyapunov Functionals , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[24]  S. Bhat,et al.  Finite-time stability of homogeneous systems , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[25]  Liqun Zhou Delay-Dependent Exponential Synchronization of Recurrent Neural Networks with Multiple Proportional Delays , 2014, Neural Processing Letters.

[26]  Mona E. Zaghloul,et al.  SYNCHRONIZATION OF CHAOTIC NEURAL NETWORKS AND APPLICATIONS TO COMMUNICATIONS , 1996 .

[27]  Tao Li,et al.  Finite-Time Consensus for Leader-Following Second-Order Multi-Agent Networks , 2012, IEEE Transactions on Circuits and Systems I: Regular Papers.

[28]  Xiaona Song,et al.  Event-triggered H∞ control for networked discrete-time Markov jump systems with repeated scalar nonlinearities , 2017, Appl. Math. Comput..

[29]  Chuandong Li,et al.  Robust Exponential Stability of Uncertain Delayed Neural Networks With Stochastic Perturbation and Impulse Effects , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[30]  Hao Shen,et al.  Finite-time reliable L 2 - Lα/Hα control for Takagi-Sugeno fuzzy systems with actuator faults , 2014 .

[31]  Hee-Jun Kang,et al.  Robust adaptive chatter-free finite-time control method for chaos control and (anti-)synchronization of uncertain (hyper)chaotic systems , 2015 .

[32]  Jinde Cao,et al.  Finite-time stochastic synchronization of complex networks , 2010 .