Global exponential periodicity and stability of memristor-based complex-valued delayed neural networks
Dan Liu, Song Zhu, Er Ye · International Journal of Systems Science · 2017
This paper concentrates on the dynamical behaviours of memristor-based complex-valued delayed neural networks. By constructing the appropriate Lyapunov functional and utilising some inequality techniques, sufficient conditions are proposed to guarantee the existence and global exponential stability of the periodic solution of the considered system. The proposed results not only generalise some previously related literatures, but also are easy to be checked with the parameters of system itself. In addition, the theoretical results of this paper may be helpful in qualitative analysis for complex-valued nonlinear delayed systems. A numerical example is given to demonstrate the effectiveness of the proposed results.