Finite-time parameter identification and adaptive synchronization between two chaotic neural networks

Abstract This work presents an approach for finite-time synchronization to identify all the unknown parameters for two coupled neural networks with time delay. Based on the finite-time stability theory, an effective feedback control with an updated law is designed to finite-time synchronization between two chaotic neural networks. Since finite-time topology identification means the suboptimum in the identification time, the results of this paper are important. Finally, an illustrative example is given to show the effectiveness of the main results.

[1]  V. Haimo.  Finite time controllers , 1986 .

[2]  X. Shan,et al.  A linear feedback synchronization theorem for a class of chaotic systems , 2002 .

[3]  Xuyang Lou,et al.  Asymptotic synchronization of a class of neural networks with reaction-diffusion terms and time-varying delays , 2006, Comput. Math. Appl..

[4]  Zhong-Ping Jiang,et al.  Topology identification of complex dynamical networks. , 2010, Chaos.

[5]  Xuyang Lou,et al.  Synchronization of neural networks based on parameter identification and via output or state coupling , 2008 .

[6]  Wuneng Zhou,et al.  Topology identification and adaptive synchronization of uncertain complex networks with adaptive double scaling functions , 2011 .

[7]  Song Zheng,et al.  Adaptive projective synchronization in complex networks with time-varying coupling delay , 2009 .

[8]  Carroll,et al.  Synchronization in chaotic systems. , 1990, Physical review letters.

[9]  Wilfrid Perruquetti,et al.  Finite-time stability and stabilization of time-delay systems , 2008, Syst. Control. Lett..

[10]  Huaguang Zhang,et al.  Adaptive Synchronization Between Two Different Chaotic Neural Networks With Time Delay , 2007, IEEE Transactions on Neural Networks.

[11]  Ju H. Park,et al.  H∞ synchronization of chaotic systems via dynamic feedback approach , 2008 .

[12]  Xiaoshan Zhao,et al.  Generalized function projective synchronization of two different hyperchaotic systems with unknown parameters , 2011 .

[13]  Rongwei Guo,et al.  Finite time stabilization of chaotic systems via single input , 2010 .

[14]  Haipeng Peng,et al.  Identifying topology of synchronous networks by analyzing their transient processes , 2012 .

[15]  U. Vincent,et al.  Finite-time synchronization for a class of chaotic and hyperchaotic systems via adaptive feedback controller , 2011 .

[16]  Liang Chen,et al.  Adaptive synchronization between two complex networks with nonidentical topological structures , 2008 .

[17]  Ulrich Parlitz,et al.  Estimating parameters by autosynchronization with dynamics restrictions. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  Luo Runzi,et al.  Finite-time stochastic combination synchronization of three different chaotic systems and its application in secure communication. , 2012, Chaos.

[19]  Wei Zhang,et al.  Finite-time chaos synchronization of unified chaotic system with uncertain parameters , 2009 .

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

[21]  Wanli Guo,et al.  Topology identification of the complex networks with non-delayed and delayed coupling , 2009 .

[22]  Jun-Juh Yan,et al.  Adaptive synchronization for two identical generalized Lorenz chaotic systems via a single controller , 2009 .

[23]  Jun-an Lu,et al.  Topology identification of weighted complex dynamical networks , 2007 .

[24]  Yuhua Xu,et al.  Topology identification of the modified complex dynamical network with non-delayed and delayed coupling , 2012 .

[25]  Fei Liu,et al.  Stochastic finite-time boundedness of Markovian jumping neural network with uncertain transition probabilities , 2011 .

[26]  Ju H. Park,et al.  Adaptive lag synchronization for uncertain complex dynamical network with delayed coupling , 2012, Appl. Math. Comput..

[27]  Jinde Cao,et al.  Adaptive synchronization of a class of chaotic neural networks with known or unknown parameters , 2008 .

[28]  Dequan Li,et al.  Synchronization and anti-synchronization of new uncertain fractional-order modified unified chaotic systems via novel active pinning control , 2010 .

[29]  Teh-Lu Liao,et al.  Adaptive synchronization of chaotic systems and its application to secure communications , 2000 .

[30]  M. P. Aghababa,et al.  Finite-time synchronization of two different chaotic systems with unknown parameters via sliding mode technique , 2011 .

[31]  Yu Tang,et al.  Terminal sliding mode control for rigid robots , 1998, Autom..

[32]  Yuhua Xu,et al.  Topology identification and adaptive synchronization of uncertain complex networks with non-derivative and derivative coupling , 2010, J. Frankl. Inst..

[33]  Junan Lu,et al.  Structure identification of uncertain general complex dynamical networks with time delay , 2009, Autom..

[34]  Zhen Jia,et al.  Weight Identification of a Weighted Bipartite Graph Complex Dynamical Network with Coupling Delay , 2010 .

[35]  Xiaoqun Wu.  Synchronization-based topology identification of weighted general complex dynamical networks with time-varying coupling delay , 2008 .

[36]  Xinchu Fu,et al.  Parameter identification of dynamical networks with community structure and multiple coupling delays , 2010 .

[37]  Yanjun Shen,et al.  LMI-based finite-time boundedness analysis of neural networks with parametric uncertainties , 2008, Neurocomputing.

[38]  Ju H. Park,et al.  Discontinuous Lyapunov functional approach to synchronization of time-delay neural networks using sampled-data , 2012 .

[39]  Wuneng Zhou,et al.  Structure identification and adaptive synchronization of uncertain general complex dynamical networks , 2009 .

[40]  Dirk Aeyels,et al.  Resonances and entrainment breakup in Kuramoto models with multimodal frequency densities. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[41]  Wei Lin,et al.  Failure of parameter identification based on adaptive synchronization techniques. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[42]  Ju H. Park,et al.  Robust synchronisation of chaotic systems with randomly occurring uncertainties via stochastic sampled-data control , 2013, Int. J. Control.

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

[44]  Xiaofeng Liao,et al.  Impulsive synchronization of chaotic systems. , 2005, Chaos.

[45]  Guoliang Cai,et al.  Global synchronization of weighted cellular neural network with time-varying coupling delays , 2012 .

[46]  Zhidong Teng,et al.  Adaptive synchronization of neural networks with time-varying delay and distributed delay , 2008 .

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

[48]  Tianping Chen,et al.  Robust synchronization of delayed neural networks based on adaptive control and parameters identification , 2006 .

[49]  Debin Huang.  Synchronization-based estimation of all parameters of chaotic systems from time series. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.