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Mathematical Problems in Engineering
Volume 2013, Article ID 579693, 4 pages
http://dx.doi.org/10.1155/2013/579693
Research Article

A Wavelet-Based Robust Relevance Vector Machine Based on Sensor Data Scheduling Control for Modeling Mine Gas Gushing Forecasting on Virtual Environment

1Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2Institute of Grassland Science, Key Laboratory of Vegetation Ecology, Ministry of Education, Northeast Normal University, Changchun 130024, China

Received 20 March 2013; Revised 24 April 2013; Accepted 9 May 2013

Academic Editor: Vishal Bhatnagar

Copyright © 2013 Wang Ting et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

It is wellknown that mine gas gushing forecasting is very significant to ensure the safety of mining. A wavelet-based robust relevance vector machine based on sensor data scheduling control for modeling mine gas gushing forecasting is presented in the paper. Morlet wavelet function can be used as the kernel function of robust relevance vector machine. Mean percentage error has been used to measure the performance of the proposed method in this study. As the mean prediction error of mine gas gushing of the WRRVM model is less than 1.5%, and the mean prediction error of mine gas gushing of the RVM model is more than 2.5%, it can be seen that the prediction accuracy for mine gas gushing of the WRRVM model is better than that of the RVM model.