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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Adaptive Event-Triggered Synchronization of Reaction–Diffusion Neural Networks
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Adaptive Event-Triggered Synchronization of Reaction–Diffusion Neural Networks

机译：反应扩散神经网络的自适应事件触发同步

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摘要

This article focuses on the design of an adaptive event-triggered sampled-data control (ETSDC) mechanism for synchronization of reaction-diffusion neural networks (RDNNs) with random time-varying delays. Different from the existing ETSDC schemes with predetermined constant thresholds, an adaptive ETSDC mechanism is proposed for RDNNs. The adaptive ETSDC mechanism can be promptly adaptively adjusted since the threshold function is based on the current sampled and latest transmitted signals. Thus, the adaptive ETSDC mechanism can effectively save communication resources for RDNNs. By taking the influence of uncertain factors, the random time-varying delays are considered, which belongs to two intervals in a probabilistic way. Then, by constructing an appropriate Lyapunov-Krasovskii functional (LKF), new synchronization criteria are derived for RDNNs. By solving a set of linear matrix inequalities (LMIs), the desired adaptive ETSDC gain is obtained. Finally, the merits of the adaptive ETSDC mechanism and the effectiveness of the proposed results are verified by one numerical example.
机译：本文侧重于设计自适应事件触发的采样数据控制（ETESDC）机制，用于与随机时变延迟同步反应扩散神经网络（RDNNS）。与具有预定恒定阈值的现有ETSDC方案不同，为RDNN提出了一种自适应ETSDC机制。可以迅速自适应地调整自适应ETSDC机制，因为阈值函数基于电流采样和最新的发送信号。因此，自适应ETSDC机制可以有效地节省RDNN的通信资源。通过采取不确定因素的影响，考虑随机时变延迟，其属于概率方式的两个间隔。然后，通过构造适当的Lyapunov-Krasovskii功能（LKF），导出新的同步标准为RDNNS导出。通过求解一组线性矩阵不等式（LMI），获得所需的自适应ETSDC增益。最后，通过一个数值示例验证了自适应ETSDC机制的优点和所提出的结果的有效性。

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