Adaptive synchronization of a class of fractional-order complex-valued chaotic neural networks with time-delay

2021 ◽  
Author(s):  
Mei Li ◽  
Ruoxun Zhang ◽  
Shiping Yang
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Fractional Order ◽  
Adaptive Synchronization ◽  
Order Complex ◽  
Chaotic Neural Networks ◽  
Complex Valued

scholarly journals Adaptive Synchronization of Fractional-Order Complex-Valued Chaotic Neural Networks with Time-Delay and Unknown Parameters

Physics ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 924-941
Author(s):  
Mei Li ◽  
Ruoxun Zhang ◽  
Shiping Yang
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Fractional Order ◽  
Adaptive Synchronization ◽  
Order Complex ◽  
Unknown Parameters ◽  
Chaotic Neural Networks ◽  
Controlled Systems ◽  
The Stability ◽  
Complex Valued

The purpose of this paper is to study and analyze the concept of fractional-order complex-valued chaotic networks with external bounded disturbances and uncertainties. The synchronization problem and parameter identification of fractional-order complex-valued chaotic neural networks (FOCVCNNs) with time-delay and unknown parameters are investigated. Synchronization between a driving FOCVCNN and a response FOCVCNN, as well as the identification of unknown parameters are implemented. Based on fractional complex-valued inequalities and stability theory of fractional-order chaotic complex-valued systems, the paper designs suitable adaptive controllers and complex update laws. Moreover, it scientifically estimates the uncertainties and external disturbances to establish the stability of controlled systems. The computer simulation results verify the correctness of the proposed method. Not only a new method for analyzing FOCVCNNs with time-delay and unknown complex parameters is provided, but also a sensitive decrease of the computational and analytical complexity.

Dissipativity and stability analysis of fractional-order complex-valued neural networks with time delay

2017 ◽  
Vol 86 ◽  
pp. 42-53 ◽  
Author(s):  
G. Velmurugan ◽  
R. Rakkiyappan ◽  
V. Vembarasan ◽  
Jinde Cao ◽  
Ahmed Alsaedi
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Time Delay ◽  
Fractional Order ◽  
Order Complex ◽  
Complex Valued

scholarly journals Stability and Hopf Bifurcation of Fractional-Order Complex-Valued Neural Networks With Time-Delay

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 158798-158807 ◽  
Author(s):  
Xiaohong Wang ◽  
Zhen Wang ◽  
Xianggeng Zhu ◽  
Bo Meng ◽  
Jianwei Xia
Keyword(s):  
Neural Networks ◽  
Hopf Bifurcation ◽  
Time Delay ◽  
Fractional Order ◽  
Order Complex ◽  
Complex Valued

Global asymptotic stability of impulsive fractional-order complex-valued neural networks with time delay

2017 ◽  
Vol 243 ◽  
pp. 49-59 ◽  
Author(s):  
Limin Wang ◽  
Qiankun Song ◽  
Yurong Liu ◽  
Zhenjiang Zhao ◽  
Fuad E. Alsaadi
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Asymptotic Stability ◽  
Fractional Order ◽  
Global Asymptotic Stability ◽  
Order Complex ◽  
Complex Valued

scholarly journals Robust Asymptotical Stability and Stabilization of Fractional-Order Complex-Valued Neural Networks with Delay

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Jingjing Zeng ◽  
Xujun Yang ◽  
Lu Wang ◽  
Xiaofeng Chen
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Fractional Order ◽  
Asymptotical Stability ◽  
Order Complex ◽  
Practical Application ◽  
Stability And Stabilization ◽  
Complex Valued ◽  
Fractional Orders ◽  
Theoretical Results

The robust asymptotical stability and stabilization for a class of fractional-order complex-valued neural networks (FCNNs) with parametric uncertainties and time delay are considered in this paper. It is worth noting that our system combines complex numbers, uncertain parameters, time delay, and fractional orders, which is universal in practical application. Using the theorem of homeomorphism, the sufficient condition of the existence and uniqueness of the equilibrium point for the system is obtained. Then, the sufficient criteria of robust asymptotical stability and stabilization for the addressed models are established, respectively. Finally, we give two numerical examples to verify the feasibility and effectiveness of the theoretical results.

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