Robust Asymptotic Stability and Projective Synchronization of Time-Varying Delayed Fractional Neural Networks Under Parametric Uncertainty

Mengqi Li, Xujun Yang, Qiankun Song, Xiaofeng Chen

Abstract: In this paper, the robust asymptotic stability and projective synchronization of fractional-order time-varying delayed neural networks with uncertain parameters are studied. On account of the homeomorphism mapping theorem, free-weighting method and generalized Halanay inequality, several sufficient conditions of existence, uniqueness and asymptotic stability of the equilibrium point of the addressed models in the form of LMIs are established. In addition, some criteria ensuring the robust asymptotic projective synchronization between the master system and the slave system are deduced based on a suitable controller. Finally, two numerical simulations are designed to illustrate the effectiveness and rationality of the theoretical results.