Stochastic exponential synchronization of memristive neural networks with time-varying delays via quantized control

Stochastic exponential synchronization of memristive neural networks with time-varying delays via quantized control
复制标题

通过量化控制具有时变延迟的忆阻神经网络的随机指数同步

DOI:
10.1016/j.neunet.2018.04.010
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发表时间:
2018-08
期刊:
影响因子:
7.8
通讯作者:
Xinsong Yang
Xinsong Yang
中科院分区:
计算机科学1区
文献类型:
--
作者:
Wanli Zhang;Shiju Yang;Chu;ong Li;Wei Zhang;Xinsong Yang

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利用Filippov解的概念建立了具有区间参数的时滞忆阻神经网络(MNN)的随机指数同步问题.构造了新的间歇控制器和对数量化的自适应控制器,以同时处理时变时滞、区间参数以及随机扰动引起的困难。此外,使用这些控制器不仅可以降低控制成本,而且可以节省通信信道和带宽。基于新的李雅普诺夫函数和新的分析方法,建立了几个同步准则,通过间歇控制和有或无对数量化的自适应控制来实现具有随机扰动的MNN的指数同步。最后,数值模拟证实了我们的理论结果。
This paper focuses on stochastic exponential synchronization of delayed memristive neural networks (MNNs) by the aid of systems with interval parameters which are established by using the concept of Filippov solution. New intermittent controller and adaptive controller with logarithmic quantization are structured to deal with the difficulties induced by time-varying delays, interval parameters as well as stochastic perturbations, simultaneously. Moreover, not only control cost can be reduced but also communication channels and bandwidth are saved by using these controllers. Based on novel Lyapunov functions and new analytical methods, several synchronization criteria are established to realize the exponential synchronization of MNNs with stochastic perturbations via intermittent control and adaptive control with or without logarithmic quantization. Finally, numerical simulations are offered to substantiate our theoretical results.
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