Design and analysis of a noise-suppression zeroing neural network approach for robust synchronization of chaotic systems

Robust synchronization of chaotic systems with time-varying external disturbances is a hot topic in the field of science and engineering. In view of the negative influence of complex noise on the synchronization of chaotic systems, a noise-suppression zeroing neural network (NSZNN) is designed and proposed to effectively resist time-varying external disturbances. Compared with existing zeroing neural network models only for bounded noise, the proposed (NSZNN) model has consistent robustness for both bounded and unbounded noises. Furthermore, the design process, theoretical analysis and numerical verification of the NSZNN are presented in detail. Both theoretical and numerical results show that the NSZNN has better synchronization control performance of chaotic systems under bounded and unbounded noises, as compared with existing zeroing neural network models. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This paper presents a noise-suppression zeroing neural network (NSZNN) to effectively resist time-varying external disturbances in chaotic systems, demonstrating consistent robustness for both bounded and unbounded noises. The NSZNN shows better synchronization control performance under bounded and unbounded noises compared with existing zeroing neural network models, as confirmed by theoretical and numerical analysis.