Abstract
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).