General decay synchronization of delayed BAM neural networks via nonlinear feedback control

Abstract This paper studies the general decay synchronization of a class of bidirectional associative memory neural networks with time-varying delays. First, a useful lemma which generalizes the classical exponential synchronization and polynomial synchronization is introduced. Then by using this lemma, some simple sufficient criteria ensuring the general decay synchronization of considered bidirectional associative memory neural networks are obtained via designing a novel nonlinear feedback controller and using some inequality techniques. Finally, two numerical examples are provided to demonstrate the feasibility of the established theoretical results. The results of this paper are general since the classical polynomial synchronization and exponential synchronization can be seen the special cases of general decay synchronization.

[1]  Shouming Zhong,et al.  Event-triggered sampling control for stability and stabilization of memristive neural networks with communication delays , 2017, Appl. Math. Comput..

[2]  Hongyong Zhao Global stability of bidirectional associative memory neural networks with distributed delays , 2002 .

[3]  Hieu Trinh,et al.  New generalized Halanay inequalities with applications to stability of nonlinear non-autonomous time-delay systems , 2015 .

[4]  Ju H. Park A novel criterion for global asymptotic stability of BAM neural networks with time delays , 2006 .

[5]  Jinde Cao,et al.  Matrix measure strategies for stability and synchronization of inertial BAM neural network with time delays , 2014, Neural Networks.

[6]  Jinde Cao,et al.  Existence and stability of almost periodic solution for BAM neural networks with delays , 2003, Appl. Math. Comput..

[7]  R. Konnur Synchronization-based approach for estimating all model parameters of chaotic systems. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Haijun Jiang,et al.  The existence and stability of the anti-periodic solution for delayed Cohen-Grossberg neural networks with impulsive effects , 2015, Neurocomputing.

[9]  K. Gopalsamy,et al.  Stability of artificial neural networks with impulses , 2004, Appl. Math. Comput..

[10]  Leon O. Chua,et al.  EXPERIMENTAL CHAOS SYNCHRONIZATION IN CHUA'S CIRCUIT , 1992 .

[11]  Ju H. Park,et al.  Synchronization for delayed memristive BAM neural networks using impulsive control with random nonlinearities , 2015, Appl. Math. Comput..

[12]  Q. Song,et al.  Global exponential stability of impulsive Cohen-Grossberg neural network with time-varying delays , 2008 .

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

[14]  Jinde Cao,et al.  LMI-based approach for delay-dependent exponential stability analysis of BAM neural networks , 2005 .

[15]  Benedetta Lisena,et al.  Exponential stability of Hopfield neural networks with impulses , 2011 .

[16]  Jinde Cao,et al.  New conditions for global exponential stability of cellular neural networks with delays , 2005, Neural Networks.

[17]  Shouming Zhong,et al.  Finite-time Mittag-Leffler synchronization of fractional-order memristive BAM neural networks with time delays , 2017, Neurocomputing.

[18]  Haijun Jiang,et al.  General decay synchronization of memristor-based Cohen-Grossberg neural networks with mixed time-delays and discontinuous activations , 2017, J. Frankl. Inst..

[19]  Abdujelil Abdurahman New results on the general decay synchronization of delayed neural networks with general activation functions , 2018, Neurocomputing.

[20]  Jinde Cao,et al.  Exponential stability and periodic oscillatory solution in BAM networks with delays , 2002, IEEE Trans. Neural Networks.

[21]  M. Feki An adaptive chaos synchronization scheme applied to secure communication , 2003 .

[22]  M. T. Yassen,et al.  Chaos synchronization between two different chaotic systems using active control , 2005 .

[23]  Xinzhi Liu,et al.  A novel approach to stability and stabilization of fuzzy sampled-data Markovian chaotic systems , 2017, Fuzzy Sets Syst..

[24]  Quanxin Zhu,et al.  Exponential synchronization of Markovian jumping chaotic neural networks with sampled-data and saturating actuators , 2017 .

[25]  Yong Li,et al.  Matrix measure strategies for stabilization and synchronization of delayed BAM neural networks , 2016 .

[26]  R. Rakkiyappan,et al.  Synchronization of memristor-based delayed BAM neural networks with fractional-order derivatives , 2016, Complex..

[27]  K. Gopalsamy,et al.  Stability in asymmetric Hopfield nets with transmission delays , 1994 .

[28]  BART KOSKO,et al.  Bidirectional associative memories , 1988, IEEE Trans. Syst. Man Cybern..

[29]  Guodong Zhang,et al.  General decay synchronization stability for a class of delayed chaotic neural networks with discontinuous activations , 2016, Neurocomputing.

[30]  Lihong Huang,et al.  Dissipativity and Synchronization of Generalized BAM Neural Networks With Multivariate Discontinuous Activations , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[31]  Yu Zhang,et al.  Stability of impulsive neural networks with time delays , 2005 .

[32]  Guodong Zhang,et al.  Synchronization of a Class of Switched Neural Networks with Time-Varying Delays via Nonlinear Feedback Control , 2016, IEEE Transactions on Cybernetics.

[33]  Shouming Zhong,et al.  Novel master-slave synchronization criteria of chaotic Lur'e systems with time delays using sampled-data control , 2017, J. Frankl. Inst..

[34]  Hsien-Keng Chen,et al.  Global chaos synchronization of new chaotic systems via nonlinear control , 2005 .

[35]  Chuan Chen,et al.  Fixed-time synchronization of memristor-based BAM neural networks with time-varying discrete delay , 2017, Neural Networks.