Affiliations: The State Key Laboratory for Manufacturing System Engineering, and System Engineering Institute, Xi'an Jiaotong University, Xi'an Shaanxi, China | Institute of Nonlinear Science, Xianyang Normal University, Xianyang, China | School of Science, Xidian University, Xi'an Shaanxi, China
Note: [] Corresponding author. Weiyuan Zhang, The State Key Laboratory for Manufacturing System Engineering, and System Engineering Institute, Xi'an Jiaotong University, Xi'an Shaanxi 710049, China. Tel.: +86 29 826679644; Fax: +86 29 82668677; E-mail: ahzwy@163.com
Abstract: A new adaptive learning control strategy is used to solve the synchronization problem for delayed reaction-diffusion fuzzy cellular neural networks with unknown periodically time-varying parameters. By constructing suitable Lyapunov-Krasovskii-like composite energy functional and employing some analysis techniques, a differential-difference mixed parametric learning law and an adaptive learning control law are constructed to achieve the adaptive synchronization of reaction-diffusion fuzzy cellular neural networks with unknown periodically time-varying parameters. Finally, a numerical example is presented to show the effectiveness of the proposed synchronization approach.
