In this paper, the stability and periodicity of memristor-based neural networks with time-varying delays are studied. Based on linear matrix inequalities, differential inclusion theory and by constructing proper Lyapu...In this paper, the stability and periodicity of memristor-based neural networks with time-varying delays are studied. Based on linear matrix inequalities, differential inclusion theory and by constructing proper Lyapunov functional approach and using linear matrix inequality, some sufficient conditions are obtained for the global exponential stability and periodic solutions of memristor-based neural networks. Finally, two illustrative examples are given to demonstrate the results.展开更多
基金This work is supported by Fundamental Research Funds for the Central Universities （Grant Nos. XDJK2016BC137, SWU116004）, Natural Science Foundation of China （Grant Nos. 61374078, 61403313, 61633011）. This publication was made possible by NPRP Grant NPRP 7-1482-1-278 from the Qatar National Research Fund （a member of Qatar Foundation）.
文摘In this paper, the stability and periodicity of memristor-based neural networks with time-varying delays are studied. Based on linear matrix inequalities, differential inclusion theory and by constructing proper Lyapunov functional approach and using linear matrix inequality, some sufficient conditions are obtained for the global exponential stability and periodic solutions of memristor-based neural networks. Finally, two illustrative examples are given to demonstrate the results.
