Finite-time synchronization of fractional-order memristive recurrent neural networks with discontinuous activation functions

Abstract This paper is concerned with the finite-time synchronization for a class of drive-response fractional-order memristive recurrent neural networks with discontinuous activation functions. By using the theories of fractional-order differential inclusions and set-valued map, the finite-time synchronization problem for a class of drive-response fractional-order memristive recurrent neural networks with discontinuous activation functions is formulated under the framework of Filippov solution. Then, two novel state feedback controllers are designed according to state feedback control technique. In particular, based on the fractional Lyapunov stability theory, the finite-time stability theory and Young inequality, some novel algebraic synchronization criteria are obtained to ensure the finite-time synchronization of a class of drive-response fractional-order memristive recurrent neural networks with discontinuous activation functions. Moreover, we give the estimation of the upper bound of the settling time for synchronization. Finally, a simulation example is given to show the effectiveness of our theoretical results.

[1]  Igor Podlubny,et al.  Mittag-Leffler stability of fractional order nonlinear dynamic systems , 2009, Autom..

[2]  Jinde Cao,et al.  Existence and Uniform Stability Analysis of Fractional-Order Complex-Valued Neural Networks With Time Delays , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Chuandong Li,et al.  Robust stability of stochastic fuzzy delayed neural networks with impulsive time window , 2015, Neural Networks.

[4]  Jinde Cao,et al.  Stability and synchronization of memristor-based fractional-order delayed neural networks , 2015, Neural Networks.

[5]  Minghui Jiang,et al.  New results on exponential synchronization of memristor-based chaotic neural networks , 2015, Neurocomputing.

[6]  Guanrong Chen,et al.  Secure synchronization of a class of chaotic systems from a nonlinear observer approach , 2005, IEEE Transactions on Automatic Control.

[7]  Tianping Chen,et al.  Dynamical behaviors of Cohen-Grossberg neural networks with discontinuous activation functions , 2005, Neural Networks.

[8]  Jinde Cao,et al.  Dynamics in fractional-order neural networks , 2014, Neurocomputing.

[9]  Michael Egmont-Petersen,et al.  Image processing with neural networks - a review , 2002, Pattern Recognit..

[10]  Zhigang Zeng,et al.  Global asymptotical stability analysis for a kind of discrete-time recurrent neural network with discontinuous activation functions , 2016, Neurocomputing.

[11]  Yaonan Wang,et al.  Robust exponential stability criterion for uncertain neural networks with discontinuous activation functions and time-varying delays , 2010, Neurocomputing.

[12]  Xiao Peng,et al.  Global synchronization in finite time for fractional-order neural networks with discontinuous activations and time delays , 2017, Neural Networks.

[13]  Yongguang Yu,et al.  Mittag-Leffler stability of fractional-order Hopfield neural networks , 2015 .

[14]  Zhigang Zeng,et al.  Exponential Adaptive Lag Synchronization of Memristive Neural Networks via Fuzzy Method and Applications in Pseudorandom Number Generators , 2014, IEEE Transactions on Fuzzy Systems.

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

[16]  Frank C. Hoppensteadt,et al.  Pattern recognition via synchronization in phase-locked loop neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..

[17]  Chuan Chen,et al.  Finite-time synchronization of memristor-based neural networks with mixed delays , 2017, Neurocomputing.

[18]  Xiaofan Li,et al.  Exponential adaptive synchronization of stochastic memristive chaotic recurrent neural networks with time-varying delays , 2017, Neurocomputing.

[19]  Hari M. Srivastava,et al.  Applications of fractional calculus to parabolic starlike and uniformly convex functions , 2000 .

[20]  Zhigang Zeng,et al.  Global Mittag–Leffler Stabilization of Fractional-Order Memristive Neural Networks , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[21]  Yangquan Chen,et al.  Computers and Mathematics with Applications Stability of Fractional-order Nonlinear Dynamic Systems: Lyapunov Direct Method and Generalized Mittag–leffler Stability , 2022 .

[22]  Yang Tang,et al.  Synchronization of Nonlinear Dynamical Networks With Heterogeneous Impulses , 2014, IEEE Transactions on Circuits and Systems I: Regular Papers.

[23]  Guoguang Wen,et al.  Stability analysis of fractional-order Hopfield neural networks with time delays , 2014, Neural Networks.

[24]  Huijun Gao,et al.  Distributed Synchronization in Networks of Agent Systems With Nonlinearities and Random Switchings , 2013, IEEE Transactions on Cybernetics.

[25]  Xinzhi Liu,et al.  Global convergence of neural networks with mixed time-varying delays and discontinuous neuron activations , 2012, Inf. Sci..

[26]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[27]  M. Krstić,et al.  Stochastic nonlinear stabilization—I: a backstepping design , 1997 .

[28]  Massimiliano Di Ventra,et al.  Experimental demonstration of associative memory with memristive neural networks , 2009, Neural Networks.

[29]  Yulong Xu,et al.  Finite-time synchronization of Markovian jump complex networks with partially unknown transition rates , 2014, J. Frankl. Inst..

[30]  Jinde Cao,et al.  Asymptotic synchronization for stochastic memristor-based neural networks with noise disturbance , 2016, J. Frankl. Inst..

[31]  Yuechao Ma,et al.  Synchronization for complex dynamical networks with mixed mode-dependent time delays , 2016, Advances in Difference Equations.

[32]  Chuandong Li,et al.  Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control , 2016, Neural Networks.

[33]  Nikola Kasabov,et al.  Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition. , 2013, Neural networks : the official journal of the International Neural Network Society.

[34]  Yang Tang,et al.  Exponential Synchronization of Coupled Switched Neural Networks With Mode-Dependent Impulsive Effects , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[35]  Haijun Jiang,et al.  Corrigendum to "Projective synchronization for fractional neural networks" , 2015, Neural Networks.

[36]  Jigui Jian,et al.  Finite-time synchronization of inertial memristive neural networks with time-varying delays via sampled-date control , 2017, Neurocomputing.

[37]  Zhen Li,et al.  Synchronization of switched complex dynamical networks with non-synchronized subnetworks and stochastic disturbances , 2016, Neurocomputing.

[38]  Wilfrid Perruquetti,et al.  Finite-Time Observers: Application to Secure Communication , 2008, IEEE Transactions on Automatic Control.

[39]  Jinde Cao,et al.  New synchronization criteria for memristor-based networks: Adaptive control and feedback control schemes , 2015, Neural Networks.

[40]  Yang Tang,et al.  Stability analysis of switched stochastic neural networks with time-varying delays , 2014, Neural Networks.

[41]  Wenbing Zhang,et al.  Finite-time cluster synchronisation of Markovian switching complex networks with stochastic perturbations , 2014 .

[42]  Anita Alaria,et al.  Applications of Fractional Calculus , 2018 .

[43]  Junzhi Yu,et al.  Global stability analysis of fractional-order Hopfield neural networks with time delay , 2015, Neurocomputing.

[44]  Jinde Cao,et al.  Synchronization of fractional-order complex-valued neural networks with time delay , 2016, Neural Networks.

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

[46]  Ivo Petrás,et al.  A Note on the Fractional-Order Cellular Neural Networks , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[47]  Wai Keung Wong,et al.  Stochastic Stability of Delayed Neural Networks With Local Impulsive Effects , 2015, IEEE Transactions on Neural Networks and Learning Systems.