Global exponential stability of stochastic memristor-based complex-valued neural networks with time delays
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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.
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