Abstract Accurate fault prognosis of track circuit is important to maintain railway signaling system. A method widely used is fuzzy neural network (FNN). However, a typical problem with such method is the lack of flexibility of the fuzzy operators. A practical solution is introducing the generalized weighted average (GWA) operator to replace the transfer functions of neurons in rule and output layers. The strength of logic operation would be adjusted by compensation parameters so as to simulate the flexibility of human thinking. In this paper, we propose a fault prognosis model based on GWA-FNN and deduce the iterative algorithm of training parameters. The simulation results show that the fault prognosis of track circuit based on GWA-FNN has better accuracy and generalization ability compared to Sum–Prod model.
Fault Prognosis of Track Circuit Based on GWA Fuzzy Neural Network
1st ed. 2016
2016-01-01
9 pages
Article/Chapter (Book)
Electronic Resource
English
Fault isolation, prognosis, and mitigation for vehicle component electrical power circuit
European Patent Office | 2021
|Track Predition Based on CNN-GRU Neural Network
IEEE | 2023
|A fault diagnosis method for the tuning area of jointless track circuits based on a neural network
SAGE Publications | 2013
|A fault diagnosis method for the tuning area of jointless track circuits based on a neural network
Online Contents | 2013
|