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Stochastic reliable synchronization for coupled Markovian reaction-diffusion neural networks with actuator failures and generalized switching policies

机译:具有执行器故障的耦合马尔维亚反应扩散神经网络的随机可靠同步和广义切换策略

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This paper is concerned with the synchronization of coupled Markovian reaction-diffusion neural networks (RDNNs) with actuator failures and generalized switching policies (GSPs) via mode-dependent reliable control. Different from some existing results with all known transition rates, GSPs are considered for coupled Markovian RDNNs, where each transition rate can be completely unknown or only its estimation is known, or each transition rate of some modes is completely unknown. To reflect more realistic behaviors, actuator failures are considered for coupled Markovian RDNNs, and a mode-dependent reliable control scheme is proposed. Then, a new Lyapunov-Krasovskii functional (LKF) is introduced, which fully utilizes the information on the slope of neuron activation functions. Based on the LKF, a synchronization criterion is established in the form of linear matrix inequalities (LMIs). Moreover, the mode-dependent reliable control gains are obtained. Finally, a numerical example is given to verify the effectiveness of the proposed results. (c) 2019 Elsevier Inc. All rights reserved.
机译:本文涉及通过模式相关的可靠控制与致动器故障和广义切换策略(GSP)的耦合的马尔科维亚反应扩散神经网络(RDNNS)的同步。与所有已知的转换速率的一些现有结果不同,GSP被认为是耦合的Markovian RDNN,其中每个过渡率可以是完全未知的,或者只知道其估计,或者一些模式的每个过渡率完全未知。为了反映更现实的行为,耦合Markovian RDNN的执行器故障被认为是提出了一种模式相关的可靠控制方案。然后,引入了新的Lyapunov-Krasovskii功能(LKF),它充分利用了神经元激活功能斜率的信息。基于LKF,以线性矩阵不等式(LMI)的形式建立同步标准。此外,获得了模式依赖性可靠的控制增益。最后,给出了一个数值例子来验证所提出的结果的有效性。 (c)2019 Elsevier Inc.保留所有权利。

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