Application and Research of the Train Fault Diagnosis Based on Improved BP Neural Network Algorithm

Abstract

Traditional BP model of neural network is easy to get a local minimum rather than the global optimal solution. As the training times increases, the learning efficiency is falling low, so as the convergence rate. Improvement on the traditional model of BP neural network algorithm improves the convergence rate of the neural network, and reduces the training times, so that the output of the neural network can not only determine the type of the train failure occurred, to improve the accuracy of diagnostic results, but also to diagnose within a certain range even the fault does not appear, to make the fault of train intelligent and simple. The simulation results show that the improved algorithm is effective.

Moisture prediction of municipal sludge drying process using BP neural network modeling and genetic algorithm optimization

“…The Backpropagation (BP) neural network, a multi-layer feedforward neural network employing the Backpropagation Algorithm for learning, demonstrates the typical capacity for nonlinear mapping. Through training and learning from data, it can establish the relationship between input variables and output variables without requiring an explicit mathematical model [45]. The network comprises three components: the input layer, output layer, and hidden layer(s).…”