New Methods of Finite-Time Synchronization for a Class of Fractional-Order Delayed Neural Networks

Finite-time synchronization for a class of fractional-order delayed neural networks with fractional order , and , is investigated in this paper. Through the use of Holder inequality, generalized Bernoulli inequality, and inequality skills, two sufficient conditions are considered to ensure synchronization of fractional-order delayed neural networks in a finite-time interval. Numerical example is given to verify the feasibility of the theoretical results.

[1]  Liping Chen,et al.  Finite-time stability of fractional delayed neural networks , 2015, Neurocomputing.

[2]  Junjian Huang,et al.  Finite-time control and synchronization for memristor-based chaotic system via impulsive adaptive strategy , 2016 .

[3]  E. Ahmed,et al.  On fractional order differential equations model for nonlocal epidemics , 2007, Physica A: Statistical Mechanics and its Applications.

[4]  Jian-an Fang,et al.  Finite-time synchronization problems of delayed complex networks with stochastic perturbations , 2014, 2014 International Conference on Mechatronics and Control (ICMC).

[5]  Hao Zhang,et al.  The modeling of the fractional-order shafting system for a water jet mixed-flow pump during the startup process , 2015, Commun. Nonlinear Sci. Numer. Simul..

[6]  Xiaodi Li,et al.  Synchronization of Identical and Nonidentical Memristor-based Chaotic Systems Via Active Backstepping Control Technique , 2015, Circuits Syst. Signal Process..

[7]  Junhai Luo,et al.  Adaptive Synchronization for a Class of Uncertain Fractional-Order Neural Networks , 2015, Entropy.

[8]  Xiaodi Li,et al.  Impulsive Control for Existence, Uniqueness, and Global Stability of Periodic Solutions of Recurrent Neural Networks With Discrete and Continuously Distributed Delays , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[9]  Xiaodi Li,et al.  Stabilization of Delay Systems: Delay-Dependent Impulsive Control , 2017, IEEE Transactions on Automatic Control.

[10]  Beibei Xu,et al.  Hamiltonian modeling of multi-hydro-turbine governing systems with sharing common penstock and dynamic analyses under shock load , 2016 .

[11]  R. Hilfer.  Applications Of Fractional Calculus In Physics , 2000 .

[12]  Yongguang Yu,et al.  Robust Stability Analysis of Fractional-Order Hopfield Neural Networks with Parameter Uncertainties , 2014 .

[13]  Chuandong Li,et al.  Finite-time lag synchronization of delayed neural networks , 2014, Neurocomputing.

[14]  Jian-an Fang,et al.  Finite time synchronization problems of delayed complex networks with stochastic perturbations , 2014 .

[15]  D. Willett.  Nonlinear vector integral equations as contraction mappings , 1964 .

[16]  Jinde Cao,et al.  Projective synchronization of fractional-order memristor-based neural networks , 2015, Neural Networks.

[17]  Weihua Deng,et al.  Remarks on fractional derivatives , 2007, Appl. Math. Comput..

[18]  Jinde Cao,et al.  Finite-time synchronization of fractional-order memristor-based neural networks with time delays , 2016, Neural Networks.

[19]  R. Rakkiyappan,et al.  Non‐Fragile Synchronization Control For Markovian Jumping Complex Dynamical Networks With Probabilistic Time‐Varying Coupling Delays , 2015 .

[20]  Xinzhi Liu,et al.  Fractional order Lyapunov stability theorem and its applications in synchronization of complex dynamical networks , 2014, Commun. Nonlinear Sci. Numer. Simul..

[21]  Diyi Chen,et al.  Chaotic synchronization and anti-synchronization for a novel class of multiple chaotic systems via a sliding mode control scheme , 2012 .

[22]  Hao Zhang,et al.  Dynamic analysis and modeling of a novel fractional-order hydro-turbine-generator unit , 2015 .

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

[24]  P. Butzer,et al.  AN INTRODUCTION TO FRACTIONAL CALCULUS , 2000 .

[25]  Haoyong Yu,et al.  Hybrid feedback feedforward: An efficient design of adaptive neural network control , 2016, Neural Networks.

[26]  Diyi Chen,et al.  Synchronization and anti-synchronization of fractional dynamical networks , 2015 .

[27]  Liping Chen,et al.  Synchronization of a Class of Fractional-Order Chaotic Neural Networks , 2013, Entropy.

[28]  Haijun Jiang,et al.  Finite-time synchronization of delayed neural networks with Cohen-Grossberg type based on delayed feedback control , 2014, Neurocomputing.

[29]  Fuad E. Alsaadi,et al.  Adaptive fuzzy prescribed performance controller design for a class of uncertain fractional-order nonlinear systems with external disturbances , 2017, Neurocomputing.

[30]  I. Podlubny.  Fractional differential equations , 1998 .

[31]  Zhigang Zeng,et al.  Global Mittag-Leffler stability and synchronization of memristor-based fractional-order neural networks , 2014, Neural Networks.

[32]  Yonghong Wu,et al.  Hamiltonian model and dynamic analyses for a hydro-turbine governing system with fractional item and time-lag , 2017, Commun. Nonlinear Sci. Numer. Simul..

[33]  Zhidong Teng,et al.  Finite-time synchronization for memristor-based neural networks with time-varying delays , 2015, Neural Networks.

[34]  Peifeng Niu,et al.  Global Mittag-Leffler projective synchronization for fractional-order neural networks: an LMI-based approach , 2016 .

[35]  Xiaoli Zhang,et al.  Effect of delayed impulses on input-to-state stability of nonlinear systems , 2017, Autom..