The Computer Interlocking Software System Reliability Test Based on the Monte Carlo

Qiong Guan; Han Qing Tao; Bin Huang

doi:10.4028/www.scientific.net/AMM.614.397

Paper Titles

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Low-Quality Video Images Recognize: A Self-Adapting Segmentation Algorithm
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SVMMAP Modeling of SAR Imagery for Unsupervised Segmentation with Bootstrap Sampling
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The Computer Interlocking Software System Reliability Test Based on the Monte Carlo
p.397

The Mean Value Theorem of a New Kind of Fuzzy Integral
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The Optimization Algorithm of Association Rules Mining
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 614The Computer Interlocking Software System...

The Computer Interlocking Software System Reliability Test Based on the Monte Carlo

Abstract:

The railway switch failure prediction for railway signal equipment maintenance plays an important role. The paper put forward railway switch failure prediction algorithm based on least squares support vector machine, and chose five characteristic indexes composed of railway switch failure prediction models characteristic input vectors. It reduces the dimension of input vectors, shorten the least squares support vector machine training time, and use a pruning algorithm to accelerate the computing speed maintaining a good regression performance at the same time. The experiment proved that railway switch failure prediction algorithm has strong self-learning ability and higher prediction accuracy based on least squares support vector machine. And it can accelerate the speed of switch failure prediction and improve the accuracy and reliability of railway switch failure prediction.

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Periodical:

Applied Mechanics and Materials (Volume 614)

Pages:

397-400

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.614.397

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Online since:

September 2014

Authors:

Qiong Guan*, Han Qing Tao, Bin Huang

Keywords:

Characteristic Index, Failure Prediction, Least Squares Support Vector Machine, Pruning Algorithm, Railway Switch

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* - Corresponding Author

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