文献类型：期刊文献

英文题名：Robust synchronization of chaotic systems using noise-resistant gradient neural dynamics: Design and application

作者：Wang, Guancheng[1];Yang, Liu[1];Zhuang, Fenghao[1];Han, Lingbo[1];Hao, Zhihao[2,3];Xiao, Xiuchun[1];Lin, Cong[1]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Beijing Technol & Business Univ, Sch Comp Sci & Artificial Intelligence, Beijing 100048, Peoples R China;[3]Macquarie Univ, Sch Comp, Sydney 2109, Australia

年份：2026

卷号：167

外文期刊名：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

收录：SCI-EXPANDED(收录号：WOS:001668515000001)、、EI(收录号：20260319919181)、Scopus(收录号：2-s2.0-105027473054)、WOS

基金：This work was supported in part by the program for scientific research start-up funds of Guangdong Ocean University (ID: 0603021124 01) , the Natural Science Foundation of Guangdong Province (2025A151 5011356) , the Undergraduate Innovation Team Project of Guangdong Ocean University (CXTD2024011 and JDTD2024003) , and the Guangdong Provincial Undergraduate Innovation and Entrepreneurship Training Program Project (S202510566039) .

语种：英文

外文关键词：Gradient neural dynamics; Chaotic systems; Synchronization; Noise-resistant; Encryption image

外文摘要：The synchronization of chaotic systems has found extensive applications in various fields, including secure communication, financial modeling, and image encryption. However, in practical scenarios, chaotic system dynamics are often significantly affected by external noise, which degrades trajectory stability and adversely impacts synchronization performance. Addressing noise-induced degradation has therefore become a critical research focus in chaotic system studies. As a key area of artificial intelligence, neural dynamics (ND) plays a significant role in modeling and optimizing complex systems. In this paper, a robust controller is designed to address this issue within a general master-slave chaotic system framework by employing Noise-Resistant Gradient Neural Dynamics (NRGND), which effectively mitigates the effects of noise and enhances synchronization efficacy. Notably, the controller requires only that the master and slave systems share the same dimensionality and that their system states and corresponding time derivatives are observable. In addition, theoretical analyses of the controller under various noises are conducted, demonstrating its exceptional convergence and robustness. Next, experiments conducted in representative chaotic systems under various noisy scenarios illustrate the superior performance of the NRGND-based controller in achieving synchronization. Finally, the performance of the NRGND controller was evaluated in two applications. The proposed method achieves image encryption and decryption by driving the Lorenz and Lu chaotic systems into synchronization. Furthermore, the NRGND-based robotic motion control scheme demonstrated its robustness and stability under noisy conditions, highlighting its potential for real-world engineering applications.