Adaptive Synchronization of Fractional-Order Output-Coupling Neural Networks via Quantized Output Control

This article focuses on the adaptive synchronization for a class of fractional-order coupled neural networks (FCNNs) with output coupling. The model is new for output coupling item in the FCNNs that treat FCNNs with state coupling as its particular case. Novel adaptive output controllers with logarithm quantization are designed to cope with the stability of the fractional-order error systems for the first attempt, which is also an effective way to synchronize fractional-order complex networks. Based on fractional-order Lyapunov functionals and linear matrix inequalities (LMIs) method, sufficient conditions rather than algebraic conditions are built to realize the synchronization of FCNNs with output coupling. A numerical simulation is put forward to substantiate the applicability of our results.

[1]  Guo-Ping Jiang,et al.  A State-Observer-Based Approach for Synchronization in Complex Dynamical Networks , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.

[2]  Jia Jia,et al.  Quasi-synchronisation of fractional-order memristor-based neural networks with parameter mismatches , 2017 .

[3]  Jin-Liang Wang,et al.  Local and global exponential output synchronization of complex delayed dynamical networks , 2012 .

[4]  Shouming Zhong,et al.  Quantized Sampled-Data Control for Synchronization of Inertial Neural Networks With Heterogeneous Time-Varying Delays , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[5]  Jinde Cao,et al.  Stability and synchronization of fractional-order memristive neural networks with multiple delays , 2017, Neural Networks.

[6]  Daniel Liberzon,et al.  Quantized feedback stabilization of linear systems , 2000, IEEE Trans. Autom. Control..

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

[8]  Abdesselem Boulkroune,et al.  Observer-based adaptive neural network control for a class of MIMO uncertain nonlinear time-delay non-integer-order systems with asymmetric actuator saturation , 2016, Neural Computing and Applications.

[9]  Haijun Jiang,et al.  Quasi-projective synchronization of fractional-order complex-valued recurrent neural networks , 2018, Neural Networks.

[10]  Jinde Cao,et al.  Global Synchronization in an Array of Delayed Neural Networks With Hybrid Coupling , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  Zhigang Zeng,et al.  Multiple Mittag–Leffler Stability of Fractional-Order Recurrent Neural Networks , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

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

[13]  Jianjun Bai,et al.  Quantized Observer-Based Sliding Mode Control for Networked Control Systems Via the Time-Delay Approach , 2016, Circuits Syst. Signal Process..

[14]  Dong Yue,et al.  Quantized output feedback control for networked control systems , 2008, Inf. Sci..

[15]  Zhixia Ding,et al.  Global Mittag-Leffler synchronization of fractional-order neural networks with discontinuous activations , 2016, Neural Networks.

[16]  Jinde Cao,et al.  Finite-time synchronization of fractional-order complex networks via hybrid feedback control , 2018, Neurocomputing.

[17]  Guanrong Chen,et al.  Global Synchronization of Coupled Delayed Neural Networks and Applications to Chaotic CNN Models , 2004, Int. J. Bifurc. Chaos.

[18]  Shuxue Wang,et al.  Synchronization and Robust Synchronization for Fractional-Order Coupled Neural Networks , 2017, IEEE Access.

[19]  Jinde Cao,et al.  Global synchronization in arrays of delayed neural networks with constant and delayed coupling , 2006 .

[20]  Jinde Cao,et al.  Adaptive neural output-feedback control for nonstrict-feedback time-delay fractional-order systems with output constraints and actuator nonlinearities , 2018, Neural Networks.

[21]  Jinde Cao,et al.  Undamped Oscillations Generated by Hopf Bifurcations in Fractional-Order Recurrent Neural Networks With Caputo Derivative , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[22]  Bernhard Eikens,et al.  Process identification with multiple neural network models , 1999 .

[23]  Renquan Lu,et al.  Asynchronous Dissipative State Estimation for Stochastic Complex Networks With Quantized Jumping Coupling and Uncertain Measurements , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[24]  Vladimir L. Kharitonov,et al.  Stability of Time-Delay Systems , 2003, Control Engineering.

[25]  Jinde Cao,et al.  Exponential Synchronization of Linearly Coupled Neural Networks With Impulsive Disturbances , 2011, IEEE Transactions on Neural Networks.

[26]  Haijun Jiang,et al.  Projective synchronization for fractional neural networks , 2014, Neural Networks.

[27]  Manuel A. Duarte-Mermoud,et al.  Lyapunov functions for fractional order systems , 2014, Commun. Nonlinear Sci. Numer. Simul..

[28]  Jinde Cao,et al.  Synchronization of fractional-order delayed neural networks with hybrid coupling , 2016, Complex..

[29]  Jinde Cao,et al.  Neuro-adaptive tracking control of non-integer order systems with input nonlinearities and time-varying output constraints , 2019, Inf. Sci..

[30]  Zidong Wang,et al.  Dynamic State Estimation of Power Systems With Quantization Effects: A Recursive Filter Approach , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[31]  Mao-Yin Chen,et al.  Synchronization in Complex Dynamical Networks With Random Sensor Delay , 2010, IEEE Transactions on Circuits and Systems II: Express Briefs.

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

[33]  Sergio Alvarez Rodríguez,et al.  A Dual Neural Network as an Identifier for a Robot Arm , 2015 .

[34]  Yuxia Li,et al.  Stability and Hopf Bifurcation of Fractional-Order Complex-Valued Single Neuron Model with Time Delay , 2017, Int. J. Bifurc. Chaos.

[35]  Jinde Cao,et al.  Synchronization of Randomly Coupled Neural Networks With Markovian Jumping and Time-Delay , 2013, IEEE Transactions on Circuits and Systems I: Regular Papers.

[36]  K. Cole.  ELECTRIC CONDUCTANCE OF BIOLOGICAL SYSTEMS , 1933 .

[37]  E. Sánchez,et al.  Input-to-state stability (ISS) analysis for dynamic neural networks , 1999 .

[38]  Jinde Cao,et al.  Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[39]  Malur K. Sundareshan,et al.  A recurrent neural network-based adaptive variable structure model-following control of robotic manipulators , 1995, Autom..

[40]  Zhigang Zeng,et al.  Global Asymptotic Stability and Adaptive Ultimate Mittag–Leffler Synchronization for a Fractional-Order Complex-Valued Memristive Neural Networks With Delays , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[41]  Jinde Cao,et al.  Synchronization of Coupled Markovian Reaction–Diffusion Neural Networks With Proportional Delays Via Quantized Control , 2019, IEEE Transactions on Neural Networks and Learning Systems.