Affiliations: Department of Mathematics, Yunnan University, Kunming, Yunnan, People’s Republic of China
Correspondence:
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Corresponding author. Yongkun Li, Department of Mathematics, Yunnan University, Kunming, Yunnan 650091, People’s Republic of China. [email protected].
Note: [1] This work is supported by the National Natural Sciences Foundation of People’s Republic of China under Grant No. 11861072.
Abstract: In this paper, we consider a class of quaternion-valued inertial neural networks with time-varying delays. First, by applying a continuation theorem of coincidence degree theory, we establish the existence of anti-periodic solutions of the considered neural networks. Second, by choosing a proper variable substitution, we transform the neural networks into a system of first order differential equations, and by constructing a suitable Lyapunov function, we derive a set of sufficient conditions ensuring the global exponential stability of the system. Finally, we give two examples to illustrate the effectiveness of our results.
