Improved quasi-synchronization criteria for delayed fractional-order memristor-based neural networks via linear feedback control

This paper is concerned with the quasi-synchronization of two delayed fractional-order memristor-based neural networks (FMNNs) with mismatched switching jumps via linear feedback control. The concept of asynchronous switching time interval (ASTI) is introduced first to describe when the drive-response FMNNs update their connection weights asynchronously. Under the framework of fractional-order differential inclusions, two improved quasi-synchronization criteria, expressed by algebraic conditions and LMIs conditions respectively, are established by constructing appropriate Lyapunov functionals in combination with some fractional-order differential inequalities. Different from most previously published works, the synchronization error bound can be estimated without requiring the bound of chaotic trajectories. In addition, it has been shown that the degree of mismatch between the switching jumps has an important influence on the distribution of ASTI as well as the practical synchronization error. Finally, two numerical examples are given to verify the validity and feasibility of the obtained results.