The synchronization of fractional chaotic systems with WRBF neural network
School of Information and Management, Guangxi Medical University, 530021, Nanning, China
Accepted: 11 August 2022
Published online: 22 August 2022
An improved synchronization method via sliding mode control (SMC) and wavelet radial basis function (WRBF) neural network is proposed for fractional chaotic systems with uncertain parameters and external disturbances. The sliding mode switching function is designed as the unique input of WRBF neural network, and the weight of the network can be adjusted according to the sliding mode approaching condition. Based on Lyapunov method, the stability conditions are given. The simulation results show that the proposed synchronous control method simplifies the complexity of conventional neural network control structure, and it only has one controller which is simple in design and reduces the chattering in SMC. Compared with previous RBF algorithms, it is validated that the improved algorithm has better robustness.
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