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首页> 外文期刊>Complexity >Exponential Stabilization of Coupled Hybrid Stochastic Delayed BAM Neural Networks: A Periodically Intermittent Control Method
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Exponential Stabilization of Coupled Hybrid Stochastic Delayed BAM Neural Networks: A Periodically Intermittent Control Method

机译:混合随机时滞BAM神经网络的指数镇定:周期间歇控制方法

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This paper considers exponential stabilization for a class of coupled hybrid stochastic delayed bidirectional associative memory neural networks (HSD-BAM-NN) with reaction-diffusion terms. A periodically intermittent controller is proposed to exponentially stabilize such an unstable HSD-BAM-NN, and sufficient conditions of the closed-loop BAM-NN system with exponential stabilization are derived by using Lyapunov-Krasovskii functional method, stochastic analysis techniques, and integral inequality property, which decide the basic parameters of the proposed controller. Furthermore, a framework to establish simulation algorithm with sampled states is presented to implement the stabilization controller. With a HSD-BAM-NN model of power synchronization in a photovoltaic (PV) array field, we illustrate numerical simulation results to verify the correctness and effectiveness of the proposed controller.
机译:本文考虑一类带有反应扩散项的耦合混合随机时滞双向联想记忆神经网络(HSD-BAM-NN)的指数稳定性。提出了一种周期性间歇控制器来对这种不稳定的HSD-BAM-NN进行指数稳定,并利用Lyapunov-Krasovskii泛函方法,随机分析技术和积分不等式推导了具有指数稳定的闭环BAM-NN系统的充分条件。属性,它决定所建议控制器的基本参数。此外,提出了建立具有采样状态的仿真算法的框架来实现稳定控制器。利用光伏(PV)阵列场中功率同步的HSD-BAM-NN模型,我们说明了数值仿真结果,以验证所提出控制器的正确性和有效性。

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