High-speed rail track response prediction method based on sparse Bayesian width learning

Abstract

The invention provides a high-speed rail track response prediction method based on sparse Bayesian width learning. The method comprises the steps: carrying out the linear and nonlinear feature extraction of an input temperature field variable, carrying out the maximum posteriori estimation of a hidden layer neuron node output layer weight, predicting a structure response output result, and evaluating the structure state of the track preliminarily. According to the method, the sparse Bayesian width learning method is adopted to carry out correlation mining on the data of the high-speed rail monitoring system, and the over-fitting problem of regression prediction can be effectively avoided through sparse solution of the weight w reflecting the relation between data variables. The method has the advantages of high prediction precision, high calculation speed and loose equipment hardware requirements, so that the mining of the correlation between the temperature load and the structural strain implied in a large amount of monitoring data can be realized, and meanwhile, the evolution of a monitoring data model is found in time to serve as a basis for judging the abnormal service state of the track structure.

Description

Technical field [0001] The present invention belongs to the technical field of machine learning and structural health monitoring, and in particular to a high railway orbital response prediction method based on sparsebased width learning. Background technique [0002] Today, high-speed rail technology is widely developed, it is increasingly becoming a problem that high-iron orbit is increasingly focusing on people. At present, the panel-type non-targeted track laid in domestic high-speed railway is a continuous orbit structure, which is affected by temperature and line shape. It is easy to effectively assess whether there is a state change in state change is an important topic of high-speed railway operation safety. [0003] In response to the above problems, the scientific research and engineering application of long-term monitoring techniques for high-speed railway rail structures in recent years have been launched in recent years, and the long-term monitoring of long-term monit...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of existing high-speed rail track state assessment, and propose a high-speed rail track response prediction method based on sparse Bayesian width learning

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