联锁
人工神经网络
信号(编程语言)
计算机科学
故障树分析
节点(物理)
树(集合论)
断层(地质)
二叉树
粒子群优化
树状网络
人工智能
实时计算
工程类
机器学习
可靠性工程
算法
时间复杂性
数学分析
数学
结构工程
地震学
程序设计语言
地质学
作者
Zhou Fang,LI Feng-ying
出处
期刊:Engineering research express
[IOP Publishing]
日期:2023-10-31
卷期号:5 (4): 045034-045034
被引量:1
标识
DOI:10.1088/2631-8695/ad0521
摘要
Abstract The signal control of the railway transportation system is crucial for operational safety. This paper briefly introduces the computer interlocking system for railway signal control, describes the tree-structured neural network used for fault diagnosis of the interlocking system, and introduces the particle swarm optimization (PSO) algorithm for improvement. Finally, a simulation experiment was conducted on a railway station to compare the traditional back-propagation neural network (BPNN), the support vector machine, the traditional tree-structured neural network, and the improved tree-structured neural network for fault diagnosis. It was found that the topological structure of the device distribution in the railway station could be transformed into a tree structure, and with the introduction of hidden nodes, it could become a binary tree structure where each leaf node represents a device; the improved tree-structured neural network had the highest recognition performance for both two-class tasks (determining system failure or not) and multi-class tasks (identifying fault type).
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