Development of a neural network model of an intelligent monitoring agent based on a recurrent neural network with a long chain of short-term memory elements

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Osamah Raheem, I. Aksenov, Yu. R. Redkin, A. Gorshkov, S. Sorokin, I. Atlasov, O. Kravets
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引用次数: 0

Abstract

The article continues to review the approach to designing the architecture of a distributed information monitoring system and quality management of communication services provided by the infrastructures of the Internet of Things and the industrial Internet of Things, based on solutions that support machine-to-machine and human-machine interaction. The development of a neural network model of an intelligent monitoring agent based on a recurrent neural network with a long chain of short-term memory elements is proposed. The matrix structure of the LSTM network memory cell is proposed, which takes into account the spatio-temporal correlation of load parameters associated with the time lag of its propagation and is a matrix of connectivity of LSTM network hyperparameters and accumulated values of load parameters of monitoring nodes in the vicinity of a controlled monitoring node, taking into account the characteristics of the time series of propagation of load in stationarity moments.
基于具有长链短时记忆元素的递归神经网络,开发智能监控代理的神经网络模型
文章继续回顾了物联网和工业物联网基础设施提供的分布式信息监控系统和通信服务质量管理的架构设计方法,其基础是支持机器到机器和人机交互的解决方案。本文提出了基于具有长链短时记忆元素的递归神经网络的智能监控代理神经网络模型的开发方法。提出了 LSTM 网络存储单元的矩阵结构,该结构考虑了负载参数与其传播时滞相关的时空相关性,是 LSTM 网络超参数与受控监测节点附近监测节点负载参数累积值的连接矩阵,同时考虑了负载在静止时刻传播的时间序列特征。
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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