Finite-time synchronization of inertial memristive neural networks with time-varying delays via sampled-date control

Abstract In this paper, the finite-time synchronization issues for inertial memristive neural networks with time-varying delay using hybrid feedback controller with sampled-date term are investigated. Firstly, by constructing a proper variable substitution,the original system is transformed into the first order differential system. Next, constructing a useful Lyapunov function, a suitable controller and using inequality techniques, some new testable algebraic criterion are obtained for ensuring the finite-time synchronization goal. Moreover, we discussed deeply on the relation between the parameter ξ i and estimated value of settling time in the different cases, and we can obtain the minimum estimated value of settling time. Finally, the effectiveness of the theoretical results have been illustrated via two numerical examples.

[1]  Leon O. Chua,et al.  Memristor oscillators , 2008, Int. J. Bifurc. Chaos.

[2]  Zhenjiang Zhao,et al.  Stability criterion of complex-valued neural networks with both leakage delay and time-varying delays on time scales , 2016, Neurocomputing.

[3]  Shouming Zhong,et al.  New synchronization criteria for complex delayed dynamical networks with sampled-data feedback control. , 2016, ISA transactions.

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

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

[6]  Stephen P. Boyd,et al.  Linear Matrix Inequalities in Systems and Control Theory , 1994 .

[7]  Jinde Cao,et al.  Finite-time synchronization of uncertain coupled switched neural networks under asynchronous switching , 2017, Neural Networks.

[8]  Junmin Li,et al.  Synchronization for distributed parameter NNs with mixed delays via sampled-data control , 2016, Neurocomputing.

[9]  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.

[10]  L.O. Chua,et al.  Memristive devices and systems , 1976, Proceedings of the IEEE.

[11]  Xuyang Lou,et al.  Synchronization of chaotic recurrent neural networks with time-varying delays using nonlinear feedback control , 2009 .

[12]  Haijun Jiang,et al.  Finite-time synchronization of memristor-based Cohen-Grossberg neural networks with time-varying delays , 2016, Neurocomputing.

[13]  Martin Bohner,et al.  Exponential synchronization of chaotic neural networks with mixed delays and impulsive effects via output coupling with delay feedback , 2010, Math. Comput. Model..

[14]  Yuehua Huang,et al.  Uniformly Observable and Globally Lipschitzian Nonlinear Systems Admit Global Finite-Time Observers , 2009, IEEE Transactions on Automatic Control.

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

[16]  Yanjun Shen,et al.  Finite-time synchronization control of a class of memristor-based recurrent neural networks , 2015, Neural Networks.

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

[18]  Rong Yao,et al.  Synchronization of a class of memristive neural networks with time delays via sampled-data control , 2015, Int. J. Mach. Learn. Cybern..

[19]  J. Tour,et al.  Electronics: The fourth element , 2008, Nature.

[20]  Xinzhi Liu,et al.  On designing stochastic sampled-data controller for master-slave synchronization of chaotic Lur'e system via a novel integral inequality , 2016, Commun. Nonlinear Sci. Numer. Simul..

[21]  Yue Zhang,et al.  Stability analysis of stochastic reaction–diffusion neural networks with Markovian switching and time delays in the leakage terms , 2014, Int. J. Mach. Learn. Cybern..

[22]  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.

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

[24]  R. Rakkiyappan,et al.  Synchronization and periodicity of coupled inertial memristive neural networks with supremums , 2016, Neurocomputing.

[25]  M. Zhan,et al.  Transition from intermittency to periodicity in lag synchronization in coupled Rössler oscillators. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  Guodong Zhang,et al.  Exponential synchronization of delayed memristor-based chaotic neural networks via periodically intermittent control , 2014, Neural Networks.

[27]  M. Molaei,et al.  Generalized synchronization of nuclear spin generator system , 2008 .

[28]  G. Snider,et al.  Self-organized computation with unreliable, memristive nanodevices , 2007 .

[29]  Qintao Gan,et al.  Synchronization of competitive neural networks with different time scales and time-varying delay based on delay partitioning approach , 2012, International Journal of Machine Learning and Cybernetics.

[30]  Xizhao Wang,et al.  A definition of partial derivative of random functions and its application to RBFNN sensitivity analysis , 2008, Neurocomputing.

[31]  Bin Wang,et al.  Finite-time parameter identification and adaptive synchronization between two chaotic neural networks , 2013, J. Frankl. Inst..

[32]  E. Yaz Linear Matrix Inequalities In System And Control Theory , 1998, Proceedings of the IEEE.

[33]  Jinde Cao,et al.  Global exponential stability in Lagrange sense for inertial neural networks with time-varying delays , 2016, Neurocomputing.

[34]  Zhigang Zeng,et al.  Synchronization control of a class of memristor-based recurrent neural networks , 2012, Inf. Sci..

[35]  X. Xia,et al.  Semi-global finite-time observers for nonlinear systems , 2008, Autom..

[36]  Robert M. Westervelt,et al.  Stability and dynamics of simple electronic neural networks with added inertia , 1986 .

[37]  Lei Shi,et al.  Finite-time synchronization for competitive neural networks with mixed delays and non-identical perturbations , 2016, Neurocomputing.

[38]  Chuandong Li,et al.  Stability of inertial BAM neural network with time-varying delay via impulsive control , 2015, Neurocomputing.

[39]  Zhidong Teng,et al.  Finite-time synchronization for fuzzy cellular neural networks with time-varying delays , 2016, Fuzzy Sets Syst..