Global exponential stability of stochastic memristor-based complex-valued neural networks with time delays
- 233 Downloads
In recent years, the dynamic behaviors of complex-valued neural networks have been extensively investigated in a variety of areas. This paper focuses on the stability of stochastic memristor-based complex-valued neural networks with time delays. By using the Lyapunov stability theory, Halanay inequality and Itô formula, new sufficient conditions are obtained for ensuring the global exponential stability of the considered system. Moreover, the obtained results not only generalize the previously published corresponding results as special cases for our results, but also can be checked with the parameters of system itself. Finally, simulation results in three numerical examples are discussed to illustrate the theoretical results.
KeywordsStochastic Complex-valued neural networks Memristor Global exponential stability Time delays
This work was supported by the Key Program of National Natural Science Foundation of China with Grant No. 61134012, National Natural Science Foundation of China with Grant No. 61203055, 11271146, the Postdoctoral Science Foundation of China 2014M560459.
- 12.Hu, J., Wang, J.: Global uniform asymptotic stability of memristor-based recurrent neural networks with time delays. In: 2010 International Joint Conference on Neural Networks. IJCNN 2010, Barcelona, Spain, 1–8 (2010)Google Scholar
- 19.Hu, J., Wang, J.: Global exponential periodicity and stability of discrete-time complex-valued recurrent neural networks with time-delays. Neural Netw. 144, 553–559 (2014)Google Scholar
- 39.Wen, S., Zeng, Z.: Dynamics analysis of a class of memristor-based recurrent networks with time-varying delays in the presence of strong external stimuli. Neural Process. Lett. 35, 47–59 (2012)Google Scholar
- 42.Nagamani, G., Ramasamy, S.: Dissipativity and passivity analysis for discrete-time complex-valued neural networks with time-varying delay. Cogent Math. 2, 1048580 (2015)Google Scholar