Exponential stability of memristor-based synchronous switching neural networks with time delays

In this paper, we study the existence, uniqueness and stability of memristor-based synchronous switching neural networks with time delays. Several criteria of exponential stability are given by introducing multiple Lyapunov functions. In comparison with the existing publications on simplice memristive neural networks or switching neural networks, we consider a system with a series of switchings, these switchings are assumed to be synchronous with memristive switching mechanism. Moreover, the proposed stability conditions are straightforward and convenient and can reflect the impact of time delay on the stability. Two examples are also presented to illustrate the effectiveness of the theoretical results.

[1]  Zhigang Zeng,et al.  Multistability of Recurrent Neural Networks With Time-varying Delays and the Piecewise Linear Activation Function , 2010, IEEE Transactions on Neural Networks.

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

[3]  Guodong Zhang,et al.  New Algebraic Criteria for Synchronization Stability of Chaotic Memristive Neural Networks With Time-Varying Delays , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[4]  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).

[5]  Guo-Ping Liu,et al.  New Delay-Dependent Stability Criteria for Neural Networks With Time-Varying Delay , 2007, IEEE Transactions on Neural Networks.

[6]  Wei Yang Lu,et al.  Nanoscale memristor device as synapse in neuromorphic systems. , 2010, Nano letters.

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

[8]  Sung-Mo Kang,et al.  Compact Models for Memristors Based on Charge-Flux Constitutive Relationships , 2010, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[9]  Kyungmin Kim,et al.  Memristor Applications for Programmable Analog ICs , 2011, IEEE Transactions on Nanotechnology.

[10]  Hongye Su,et al.  New results on robust exponential stability for discrete recurrent neural networks with time-varying delays , 2009, Neurocomputing.

[11]  Chuandong Li,et al.  Neural network for solving Nash equilibrium problem in application of multiuser power control , 2014, Neural Networks.

[12]  Leon O. Chua,et al.  MEMRISTOR CELLULAR AUTOMATA AND MEMRISTOR DISCRETE-TIME CELLULAR NEURAL NETWORKS , 2009 .

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

[14]  Jinde Cao,et al.  Global stability in switched recurrent neural networks with time-varying delay via nonlinear measure , 2007 .

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

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

[17]  Leon O. Chua,et al.  Circuit Elements With Memory: Memristors, Memcapacitors, and Meminductors , 2009, Proceedings of the IEEE.

[18]  Peng Shi,et al.  Robust Fault Detection for Switched Linear Systems With State Delays , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

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

[20]  A. Tesi,et al.  New conditions for global stability of neural networks with application to linear and quadratic programming problems , 1995 .

[21]  Jie Yang,et al.  New results on robust energy-to-peak filtering for discrete-time switched polytopic linear systems with time-varying delay , 2008 .

[22]  Daoyi Xu,et al.  Stability Analysis of Delay Neural Networks With Impulsive Effects , 2005, IEEE Trans. Circuits Syst. II Express Briefs.

[23]  Zhigang Zeng,et al.  Exponential stability analysis of memristor-based recurrent neural networks with time-varying delays , 2012, Neurocomputing.

[24]  Chuandong Li,et al.  Stability of switched neural networks with time delay , 2015 .

[25]  Xiaodi Li,et al.  Existence and global stability analysis of equilibrium of fuzzy cellular neural networks with time delay in the leakage term under impulsive perturbations , 2011, J. Frankl. Inst..

[26]  Chuandong Li,et al.  Neural network for solving convex quadratic bilevel programming problems , 2014, Neural Networks.

[27]  Xiaodi Li,et al.  LMI Approach for Stationary Oscillation of Interval Neural Networks With Discrete and Distributed Time-Varying Delays Under Impulsive Perturbations , 2010, IEEE Transactions on Neural Networks.

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

[29]  M. Forti,et al.  Global convergence of neural networks with discontinuous neuron activations , 2003 .

[30]  Chuandong Li,et al.  A Recurrent Neural Network for Solving Bilevel Linear Programming Problem , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[31]  Qiankun Song,et al.  Exponential stability of recurrent neural networks with both time-varying delays and general activation functions via LMI approach , 2008, Neurocomputing.

[32]  Chuandong Li,et al.  Delay-interval-dependent stability of recurrent neural networks with time-varying delay , 2009, Neurocomputing.