Synchronization analysis and parameters identification of uncertain delayed fractional-order BAM neural networks

In this paper, synchronization analysis and parameters identification issues are explored for uncertain delayed fractional-order BAM neural networks. By designing pertinent state feedback control strategies and parameters updated laws, some ample criteria are procured for ensuring the finite-time synchronization and the Mittag-Leffler synchronization of the considered networks via exploiting the Lyapunov function theory, fractional calculus theory and inequality analysis techniques, meanwhile, the settling time of finite-time synchronization is given, which relates to the initial values. Moreover, parameters identification is actualized triumphantly for uncertain or unknown parameters. Finally, numerical examples are provided to show the availability of the theoretical results.

Automated summary

This paper investigates synchronization analysis and parameters identification for uncertain delayed fractional-order BAM neural networks. By designing control strategies and parameters updated laws, using Lyapunov function theory, fractional calculus theory and inequality analysis techniques, the paper establishes criteria for ensuring finite-time synchronization and Mittag-Leffler synchronization of the considered networks. The settling time of finite-time synchronization is also provided, which is related to the initial values. Furthermore, parameter identification is successfully realized for uncertain or unknown parameters. Numerical examples are provided to demonstrate the effectiveness of the theoretical results.