Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Superelevation Prediction Method of Orbit Irregularity Based on Grey Model of Stochastic Oscillation Sequence

A track irregularity and gray model technology, applied in the field of track detection, can solve the problems of large error in the prediction result of random oscillation sequence, poor prediction effect of random oscillation sequence, and inability to fit the change trend of the sequence, and achieve good prediction ability, The effect of accurate prediction results and high prediction accuracy

Active Publication Date: 2022-07-22
JINAN UNIVERSITY
View PDF2 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Super high is different from TQI, it is a random oscillation sequence, but the traditional gray prediction is only suitable for exponential growth data types, it is not good for random oscillation sequence prediction, and cannot fit the change trend of the sequence, which leads to the random oscillation sequence Large error in prediction results

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Superelevation Prediction Method of Orbit Irregularity Based on Grey Model of Stochastic Oscillation Sequence
  • Superelevation Prediction Method of Orbit Irregularity Based on Grey Model of Stochastic Oscillation Sequence
  • Superelevation Prediction Method of Orbit Irregularity Based on Grey Model of Stochastic Oscillation Sequence

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0112] like figure 1 As shown, this embodiment provides a method for superhigh prediction of orbital irregularity based on a stochastic oscillation sequence gray model, and the method includes the following steps:

[0113] Data preprocessing step: perform mean value processing on the detected left and right rail surface height deviations to obtain an equally spaced average height deviation sequence;

[0114] Preliminary prediction steps: Based on the gray model, the random oscillation sequence gray prediction is performed on the equally spaced average height deviation sequence, and the preliminary predicted height deviation is obtained;

[0115] Prediction correction steps: Based on the initial prediction height deviation and the original data, the initial prediction height residual is obtained, the average height residual error is calculated, and the preliminary predicted height residual error is corrected based on the average height residual error to obtain the revised heigh...

Embodiment 2

[0204] The present embodiment provides an ultra-high prediction system for orbit irregularity based on a stochastic oscillation sequence gray model, and the system includes: a data preprocessing module, a preliminary prediction module, a prediction correction module, a neural network module, and an ultra-high prediction module;

[0205] The data preprocessing module is used for averaging the detected left and right rail surface height deviations to obtain an equally spaced average height deviation sequence;

[0206] The preliminary prediction module is used to perform gray prediction of random oscillation sequence based on the gray model for the average height deviation sequence of equal intervals, and obtain the preliminary predicted height deviation;

[0207] The prediction correction module is used to obtain the preliminary predicted height residual based on the preliminary predicted height deviation and the original data, calculate the average value of the height residual, ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a superelevation prediction method for track irregularity based on a random oscillation sequence grey model. The method comprises the following steps: a data preprocessing step: performing mean value processing on the detected height deviations of the left and right track surfaces to obtain an equal-spaced average height deviation sequence; preliminary prediction step: perform gray prediction of random oscillation sequence based on the gray model, and obtain the preliminary predicted height deviation; prediction correction step: correct the preliminary predicted height residual based on the average value of the height residual to obtain the modified height residual and normalize it Processing; Steps of optimizing Elman neural network: optimize the initial weights and thresholds of Elman neural network by Antlion algorithm, and then obtain the optimized Elman neural network; superelevation prediction step: obtain the track prediction correction height residual based on the optimized Elman neural network. The invention overcomes the defect of unsatisfactory prediction result of random oscillation sequence by combining the gray model of random oscillation sequence and Elman neural network, so that the prediction result of super high is more accurate.

Description

technical field [0001] The invention relates to the field of track detection, in particular to a superelevation prediction method of track irregularity based on a random oscillation sequence grey model. Background technique [0002] The detection of the safety status of urban rail transit operation is an important part of ensuring the operation of the rail. The existing methods can dynamically and accurately detect the parameters of the rail, but how to analyze and predict the quality of the rail from the data detected on the rail is very important for the rail. Detection research is critical. [0003] Most of the current research is on the prediction of the track comprehensive quality TQI. From the experimental results, the combination method of gray prediction and neural network is more accurate for its prediction; [0004] Super high is different from TQI in that it is a stochastic oscillation sequence, but the traditional gray forecast is only suitable for exponentially...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q10/06G06Q50/30G06N3/04G06N3/08
CPCG06Q10/04G06Q10/06395G06N3/04G06N3/084G06Q50/40
Inventor 谢勇君黄佳滨贺志超黎晨凡鸿儒殷怡严冬松武建华
Owner JINAN UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com