scholarly journals Synchronization of a Class of Fractional-Order Chaotic Neural Networks

Entropy ◽  
2013 ◽  
Vol 15 (12) ◽  
pp. 3265-3276 ◽  
Author(s):  
Liping Chen ◽  
Jianfeng Qu ◽  
Yi Chai ◽  
Ranchao Wu ◽  
Guoyuan Qi
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Chaotic Neural Networks

scholarly journals Adaptive Fuzzy Synchronization of Fractional-Order Chaotic Neural Networks with Backlash-Like Hysteresis

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Wenqing Fu ◽  
Heng Liu
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Control Method ◽  
Fuzzy Controller ◽  
Small Region ◽  
Fuzzy Logic Systems ◽  
Chaotic Neural Networks ◽  
Adaptive Fuzzy ◽  
Synchronization Errors ◽  
Lyapunov Stability Criterion

An adaptive fuzzy synchronization controller is designed for a class of fractional-order neural networks (FONNs) subject to backlash-like hysteresis input. Fuzzy logic systems are used to approximate the system uncertainties as well as the unknown terms of the backlash-like hysteresis. An adaptive fuzzy controller, which can guarantee the synchronization errors tend to an arbitrary small region, is given. The stability of the closed-loop system is rigorously analyzed based on fractional Lyapunov stability criterion. Fractional adaptation laws are established to update the fuzzy parameters. Finally, some simulation examples are provided to indicate the effectiveness and the robust of the proposed control method.

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.

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