Prediction of gas concentration based on residual correction of Markov chai

In view of problem of low accuracy of part of prediction values while using gray neural network for gas concentration prediction, the paper proposed a method of using Markov model to correct prediction results of three-order gray neural network model. It described establishment of gray neural networ...

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Main Authors: HAN Tingting, WU Shiyue, WANG Pengjun
Format: Article
Language:zho
Published: Editorial Department of Industry and Mine Automation 2014-03-01
Series:Gong-kuang zidonghua
Subjects:
Online Access:http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2014.03.008
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author HAN Tingting
WU Shiyue
WANG Pengjun
author_facet HAN Tingting
WU Shiyue
WANG Pengjun
author_sort HAN Tingting
collection DOAJ
description In view of problem of low accuracy of part of prediction values while using gray neural network for gas concentration prediction, the paper proposed a method of using Markov model to correct prediction results of three-order gray neural network model. It described establishment of gray neural network model and Markov residual correction method, and used the method to analyze and predict gas concentration in different locations of a coal mine at different times. Practical application results show that the maximum relative error of predicted gas concentration and measured value reduced from 14% to 6% after Markov residual correction, and the corrected gas concentration curve is closer to the actual changing trend of gas concentration.
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spelling doaj.art-64e22d1593f34675a37c04da649d18482023-03-17T01:52:10ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2014-03-01403283110.13272/j.issn.1671-251x.2014.03.008Prediction of gas concentration based on residual correction of Markov chaiHAN Tingting0WU Shiyue1WANG Pengjun2College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaShanxi Asian American Daning Energy Co., Ltd., Jincheng 048000, ChinaIn view of problem of low accuracy of part of prediction values while using gray neural network for gas concentration prediction, the paper proposed a method of using Markov model to correct prediction results of three-order gray neural network model. It described establishment of gray neural network model and Markov residual correction method, and used the method to analyze and predict gas concentration in different locations of a coal mine at different times. Practical application results show that the maximum relative error of predicted gas concentration and measured value reduced from 14% to 6% after Markov residual correction, and the corrected gas concentration curve is closer to the actual changing trend of gas concentration.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2014.03.008prediction of gas concentrationmarkov chainresidual correctiongray neural network
spellingShingle HAN Tingting
WU Shiyue
WANG Pengjun
Prediction of gas concentration based on residual correction of Markov chai
Gong-kuang zidonghua
prediction of gas concentration
markov chain
residual correction
gray neural network
title Prediction of gas concentration based on residual correction of Markov chai
title_full Prediction of gas concentration based on residual correction of Markov chai
title_fullStr Prediction of gas concentration based on residual correction of Markov chai
title_full_unstemmed Prediction of gas concentration based on residual correction of Markov chai
title_short Prediction of gas concentration based on residual correction of Markov chai
title_sort prediction of gas concentration based on residual correction of markov chai
topic prediction of gas concentration
markov chain
residual correction
gray neural network
url http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2014.03.008
work_keys_str_mv AT hantingting predictionofgasconcentrationbasedonresidualcorrectionofmarkovchai
AT wushiyue predictionofgasconcentrationbasedonresidualcorrectionofmarkovchai
AT wangpengjun predictionofgasconcentrationbasedonresidualcorrectionofmarkovchai