Impulsive Stabilization of Unconstrained Multilayer Recurrent Neural Networks with Node-Based Time-varying Delays

Xiangxiang Wang, Yongbin Yu, Xiao Feng, Xinyi Han, Jingya Wang, Jingye Cai
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Abstract

This article discusses the exponential stabilization of node-dependent delayed multilayer neural networks (NDDMNNs) under impulsive control. To address different modeling requirements in complicated applications, node-based interlayer and intralayer parameters are presented to design the neural network model, indicating that The nodes constituting the network can have different structures. Meanwhile, the novel model considers the node-dependent time-varying delays, and this article develops the sparse matrix approach to translate the node-dependent delayed NDDMNNs model into an multiple delayed model, ensuring that the vector form of NDDMNNs can be constructed and studied by using existing technical approaches. Then, an analytical framework with super-Laplacian matrix and time-dependent Lyapunov function methods is proposed to derive exponential stabilization results. Finally, a numerical simulation example is given to verify the obtained results.
基于节点时变时滞的无约束多层递归神经网络的脉冲镇定
讨论了脉冲控制下节点依赖延迟多层神经网络的指数镇定问题。针对复杂应用中不同的建模需求,提出了基于节点的层间和层内参数来设计神经网络模型,表明构成网络的节点可以具有不同的结构。同时,该模型考虑了节点依赖的时变延迟,本文发展了稀疏矩阵方法,将节点依赖的延迟NDDMNNs模型转化为多延迟模型,保证了NDDMNNs的矢量形式可以利用现有的技术方法来构建和研究。然后,利用超拉普拉斯矩阵和时变Lyapunov函数方法建立了一个分析框架,推导出指数镇定结果。最后,通过数值仿真实例验证了所得结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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