Stability analysis for discrete-time stochastic memristive neural networks with both leakage and probabilistic delays.
作者： Hongjian Liu , Zidong Wang , Bo Shen , Tingwen Huang , Fuad E. Alsaadi
DOI: 10.1016/J.NEUNET.2018.02.003
关键词:
摘要: Abstract This paper is concerned with the globally exponential stability problem for a class of discrete-time stochastic memristive neural networks (DSMNNs) both leakage delays as well probabilistic time-varying delays. For delays, sequence Bernoulli distributed random variables utilized to determine within which intervals fall at certain time instant. The sector-bounded activation function considered in addressed DSMNN. By taking into account state-dependent characteristics network parameters and choosing an appropriate Lyapunov–Krasovskii functional, some sufficient conditions are established under underlying DSMNN exponentially stable mean square. derived made dependent on therefore less conservative than traditional delay-independent criteria. A simulation example given show effectiveness proposed criterion.
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