Adaptive Synchronization Control and Parameters Identification for Chaotic Fractional Neural Networks with Time-Varying Delays

In this paper, the adaptive synchronization control and synchronization-based parameters identification method for time-varying delayed fractional chaotic neural networks are proposed. Based on the adaptive control with suitable update law and linear feedback control, an analytical, rigorous, and simple adaptive control method is given, which can make two coupled fractional-order delayed neural networks achieve synchronization. In addition, the uncertain system parameters can also be identified along with the realization of synchronization. The speed of synchronization and parameter identification can be adjusted by selecting appropriate control parameters. Besides, the proposed method is very easy to accomplish in reality and has strong robustness against external disturbances. Finally, the numerical simulations are put into practice to illustrate the rationality and validity of theoretical analysis.