A prediction method for gas emission based on RBF with grey correlation analysis

A rolling method of gas emission based on RBF neural networks is improved. In this method, a part of fixed-length data is selected for the prediction, new data are added continuously to the input sequence, and old data are removed, thereby developing the rolling prediction model. The diversified fac...

Full description

Saved in:
Bibliographic Details
Main Authors: Yumin Pan, Hongmei Ma, Quanzhu Zhang, Pengqian Xue
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Artificial neural networks
Coal
Data models
Predictive models
Training
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 154
container_issue
container_start_page 151
container_title
container_volume
creator Yumin Pan
Hongmei Ma
Quanzhu Zhang
Pengqian Xue
description A rolling method of gas emission based on RBF neural networks is improved. In this method, a part of fixed-length data is selected for the prediction, new data are added continuously to the input sequence, and old data are removed, thereby developing the rolling prediction model. The diversified factors of gas emission analyzed have grey correlation. As a result, the model designed using this method can generalize well. The simulation results also show that the improved rolling prediction model applied in gas emission prediction has reliable accuracy and a good convergence rate.
doi_str_mv 10.1109/ICMIC.2011.5973692
format conference_proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5973692</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5973692</ieee_id><sourcerecordid>5973692</sourcerecordid><originalsourceid>FETCH-LOGICAL-i90t-67bf32a45096e817867cce3931e9635691220e02259192469ee6e83e7bdd45b03</originalsourceid><addsrcrecordid>eNotj1FLwzAUheODoMz-gfmSP9B6kzRJ7-MsTgsTZex9pO3tFmnXkRSk_96pezqHw3cOHMaWAjIhAJ-q8r0qMwlCZBqtMihvWIK2ANTGCm2huGNJjF8AINAgaHXPPlf8HKj1zeTHEx9oOo4t78bADy5yGnyMv3ntIrX8YrbPa_7tpyM_BJp5M4ZAvfurupPr5-jjA7vtXB8pueqC7dYvu_It3Xy8VuVqk3qEKTW27pR0uQY0VAhbGNs0pFAJQqO0QSElEEipUaDMDRJdOEW2bttc16AW7PF_1hPR_hz84MK8v95WP3g0TME</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>A prediction method for gas emission based on RBF with grey correlation analysis</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Yumin Pan ; Hongmei Ma ; Quanzhu Zhang ; Pengqian Xue</creator><creatorcontrib>Yumin Pan ; Hongmei Ma ; Quanzhu Zhang ; Pengqian Xue</creatorcontrib><description>A rolling method of gas emission based on RBF neural networks is improved. In this method, a part of fixed-length data is selected for the prediction, new data are added continuously to the input sequence, and old data are removed, thereby developing the rolling prediction model. The diversified factors of gas emission analyzed have grey correlation. As a result, the model designed using this method can generalize well. The simulation results also show that the improved rolling prediction model applied in gas emission prediction has reliable accuracy and a good convergence rate.</description><identifier>EISBN: 9780956715708</identifier><identifier>EISBN: 0956715702</identifier><identifier>DOI: 10.1109/ICMIC.2011.5973692</identifier><language>eng</language><publisher>IEEE</publisher><subject>Accuracy ; Artificial neural networks ; Coal ; Data models ; Predictive models ; Training</subject><ispartof>Proceedings of 2011 International Conference on Modelling, Identification and Control, 2011, p.151-154</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5973692$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5973692$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Yumin Pan</creatorcontrib><creatorcontrib>Hongmei Ma</creatorcontrib><creatorcontrib>Quanzhu Zhang</creatorcontrib><creatorcontrib>Pengqian Xue</creatorcontrib><title>A prediction method for gas emission based on RBF with grey correlation analysis</title><title>Proceedings of 2011 International Conference on Modelling, Identification and Control</title><addtitle>ICMIC</addtitle><description>A rolling method of gas emission based on RBF neural networks is improved. In this method, a part of fixed-length data is selected for the prediction, new data are added continuously to the input sequence, and old data are removed, thereby developing the rolling prediction model. The diversified factors of gas emission analyzed have grey correlation. As a result, the model designed using this method can generalize well. The simulation results also show that the improved rolling prediction model applied in gas emission prediction has reliable accuracy and a good convergence rate.</description><subject>Accuracy</subject><subject>Artificial neural networks</subject><subject>Coal</subject><subject>Data models</subject><subject>Predictive models</subject><subject>Training</subject><isbn>9780956715708</isbn><isbn>0956715702</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj1FLwzAUheODoMz-gfmSP9B6kzRJ7-MsTgsTZex9pO3tFmnXkRSk_96pezqHw3cOHMaWAjIhAJ-q8r0qMwlCZBqtMihvWIK2ANTGCm2huGNJjF8AINAgaHXPPlf8HKj1zeTHEx9oOo4t78bADy5yGnyMv3ntIrX8YrbPa_7tpyM_BJp5M4ZAvfurupPr5-jjA7vtXB8pueqC7dYvu_It3Xy8VuVqk3qEKTW27pR0uQY0VAhbGNs0pFAJQqO0QSElEEipUaDMDRJdOEW2bttc16AW7PF_1hPR_hz84MK8v95WP3g0TME</recordid><startdate>201106</startdate><enddate>201106</enddate><creator>Yumin Pan</creator><creator>Hongmei Ma</creator><creator>Quanzhu Zhang</creator><creator>Pengqian Xue</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201106</creationdate><title>A prediction method for gas emission based on RBF with grey correlation analysis</title><author>Yumin Pan ; Hongmei Ma ; Quanzhu Zhang ; Pengqian Xue</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-67bf32a45096e817867cce3931e9635691220e02259192469ee6e83e7bdd45b03</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Accuracy</topic><topic>Artificial neural networks</topic><topic>Coal</topic><topic>Data models</topic><topic>Predictive models</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Yumin Pan</creatorcontrib><creatorcontrib>Hongmei Ma</creatorcontrib><creatorcontrib>Quanzhu Zhang</creatorcontrib><creatorcontrib>Pengqian Xue</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEL</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yumin Pan</au><au>Hongmei Ma</au><au>Quanzhu Zhang</au><au>Pengqian Xue</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A prediction method for gas emission based on RBF with grey correlation analysis</atitle><btitle>Proceedings of 2011 International Conference on Modelling, Identification and Control</btitle><stitle>ICMIC</stitle><date>2011-06</date><risdate>2011</risdate><spage>151</spage><epage>154</epage><pages>151-154</pages><eisbn>9780956715708</eisbn><eisbn>0956715702</eisbn><abstract>A rolling method of gas emission based on RBF neural networks is improved. In this method, a part of fixed-length data is selected for the prediction, new data are added continuously to the input sequence, and old data are removed, thereby developing the rolling prediction model. The diversified factors of gas emission analyzed have grey correlation. As a result, the model designed using this method can generalize well. The simulation results also show that the improved rolling prediction model applied in gas emission prediction has reliable accuracy and a good convergence rate.</abstract><pub>IEEE</pub><doi>10.1109/ICMIC.2011.5973692</doi><tpages>4</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier EISBN: 9780956715708
ispartof Proceedings of 2011 International Conference on Modelling, Identification and Control, 2011, p.151-154
issn
language eng
recordid cdi_ieee_primary_5973692
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Accuracy
Artificial neural networks
Coal
Data models
Predictive models
Training
title A prediction method for gas emission based on RBF with grey correlation analysis
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-22T12%3A01%3A01IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=A%20prediction%20method%20for%20gas%20emission%20based%20on%20RBF%20with%20grey%20correlation%20analysis&rft.btitle=Proceedings%20of%202011%20International%20Conference%20on%20Modelling,%20Identification%20and%20Control&rft.au=Yumin%20Pan&rft.date=2011-06&rft.spage=151&rft.epage=154&rft.pages=151-154&rft_id=info:doi/10.1109/ICMIC.2011.5973692&rft.eisbn=9780956715708&rft.eisbn_list=0956715702&rft_dat=%3Cieee_6IE%3E5973692%3C/ieee_6IE%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i90t-67bf32a45096e817867cce3931e9635691220e02259192469ee6e83e7bdd45b03%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5973692&rfr_iscdi=true