Synchronization for discrete-time memristive recurrent neural networks with time-delays

This paper is concerned with the synchronization problem for a class of discrete-time memristive recurrent neural networks with time-delays. By using the piecewise Lyapunov function technique, sufficient conditions are obtained to guarantee the exponential synchronization of the discrete-time memristive neural networks with time-delays. By recurring to the delay partitioning method and the free-weighting matrix technique, the conservatism of the obtained results is reduced. Finally, a numerical example is presented to demonstrate the effectiveness of the derived theoretical results.

[1]  S. Arik.  Stability analysis of delayed neural networks , 2000 .

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

[3]  Zhigang Zeng,et al.  Improved conditions for global exponential stability of recurrent neural networks with time-varying delays , 2006, IEEE Transactions on Neural Networks.

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

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

[6]  Andrew R. Teel,et al.  On the Robustness of KL-stability for Difference Inclusions: Smooth Discrete-Time Lyapunov Functions , 2005, SIAM J. Control. Optim..

[7]  Zidong Wang,et al.  Global exponential stability of generalized recurrent neural networks with discrete and distributed delays , 2006, Neural Networks.

[8]  Maozhen Li,et al.  Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays , 2006, IEEE Transactions on Neural Networks.

[9]  Lixiang Li,et al.  Synchronization control of memristor-based recurrent neural networks with perturbations , 2014, Neural Networks.

[10]  Jun Wang,et al.  Global uniform asymptotic stability of memristor-based recurrent neural networks with time delays , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[11]  Kanjian Zhang,et al.  Synchronization control of recurrent neural networks with distributed delays , 2008 .

[12]  Zhigang Zeng,et al.  Exponential synchronization of memristor-based recurrent neural networks with time delays , 2011, Neurocomputing.

[13]  Hong Qiao,et al.  A reference model approach to stability analysis of neural networks , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[14]  Hong Qiao,et al.  Nonlinear measures: a new approach to exponential stability analysis for Hopfield-type neural networks , 2001, IEEE Trans. Neural Networks.

[15]  Jinde Cao,et al.  Exponential Synchronization of Hybrid Coupled Networks With Delayed Coupling , 2010, IEEE Transactions on Neural Networks.

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

[17]  J. Tour,et al.  The fourth element , 2008 .

[18]  James Lam,et al.  Stability and Synchronization of Discrete-Time Neural Networks With Switching Parameters and Time-Varying Delays , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[19]  Jinde Cao,et al.  Global asymptotic stability of a general class of recurrent neural networks with time-varying delays , 2003 .

[20]  Zhigang Zeng,et al.  Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays , 2013, Neural Networks.

[21]  Zidong Wang,et al.  Robust stability of discrete-time stochastic neural networks with time-varying delays , 2008, Neurocomputing.

[22]  Zidong Wang,et al.  Stability and Synchronization of Discrete-Time Markovian Jumping Neural Networks With Mixed Mode-Dependent Time Delays , 2009, IEEE Transactions on Neural Networks.

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