The recognition of train wheel tread damages based on PSO-RBFNN algorithm

In order to the recognition of the train wheel tread damages, the pattern recognition method of the train wheel tread damages based on PSO-RBFNN was developed. The algorithm uses PSO-RBFNN algorithm to optimize center and spread of RBFNN, the connection weight value is sovled by least squares method...

Full description

Saved in:
Bibliographic Details
Main Authors: Zhao Yong, Ye Hong, Kang Zheng-sheng, Shi Song-shan, Zhou Lin
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Feature extraction
Inspection
Pattern recognition
PSORBFNN
recognition
Signal processing algorithms
train wheel
Training
tread damage
Wheels
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 1095
container_issue
container_start_page 1093
container_title
container_volume
creator Zhao Yong
Ye Hong
Kang Zheng-sheng
Shi Song-shan
Zhou Lin
description In order to the recognition of the train wheel tread damages, the pattern recognition method of the train wheel tread damages based on PSO-RBFNN was developed. The algorithm uses PSO-RBFNN algorithm to optimize center and spread of RBFNN, the connection weight value is sovled by least squares method. Compared with the traditional RBFNN,BP and GA-RBFNN, the experiment results show that the recognition rate of testing samples is higher than the traditional RBFNN, BP and GA-RBFNN, the evolutional generations of PSO-RBFNN algorithm were less than RBFNN, BP and GA-RBFNN.
doi_str_mv 10.1109/ICNC.2012.6234662
format conference_proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6234662</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6234662</ieee_id><sourcerecordid>6234662</sourcerecordid><originalsourceid>FETCH-LOGICAL-i90t-208b7d10945f6f254de914bafbe5287fee5212cbd61c2558be1ea5c2bfe186123</originalsourceid><addsrcrecordid>eNpVUMFKxDAUjKjgsvYDxEt-oDXvNWnaoxZXC0tXtPclaV_aSLeVtiD-vQX34lzmDQzDvGHsDkQEILKHIi_zCAVglGAskwQvWJDpFKTSGiHG9PKfFvKKbRCUDjOl1A0L5vlTrNAK0kRuWFF1xCeqx3bwix8HPjq-TMYP_Lsj6tebTMMbczItzdyamRq-ut4-DuH7064suenbcfJLd7pl1870MwVn3rJq91zlr-H-8FLkj_vQZ2IJUaRWN-sjUrnEoZINZSCtcZYUptrRSoC1bRKoUanUEpBRNVpHa2HAeMvu_2I9ER2_Jn8y08_xvEX8C8V6TxE</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>The recognition of train wheel tread damages based on PSO-RBFNN algorithm</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Zhao Yong ; Ye Hong ; Kang Zheng-sheng ; Shi Song-shan ; Zhou Lin</creator><creatorcontrib>Zhao Yong ; Ye Hong ; Kang Zheng-sheng ; Shi Song-shan ; Zhou Lin</creatorcontrib><description>In order to the recognition of the train wheel tread damages, the pattern recognition method of the train wheel tread damages based on PSO-RBFNN was developed. The algorithm uses PSO-RBFNN algorithm to optimize center and spread of RBFNN, the connection weight value is sovled by least squares method. Compared with the traditional RBFNN,BP and GA-RBFNN, the experiment results show that the recognition rate of testing samples is higher than the traditional RBFNN, BP and GA-RBFNN, the evolutional generations of PSO-RBFNN algorithm were less than RBFNN, BP and GA-RBFNN.</description><identifier>ISSN: 2157-9555</identifier><identifier>ISBN: 9781457721304</identifier><identifier>ISBN: 1457721309</identifier><identifier>EISBN: 9781457721328</identifier><identifier>EISBN: 1457721325</identifier><identifier>EISBN: 9781457721335</identifier><identifier>EISBN: 1457721333</identifier><identifier>DOI: 10.1109/ICNC.2012.6234662</identifier><language>eng</language><publisher>IEEE</publisher><subject>Accuracy ; Feature extraction ; Inspection ; Pattern recognition ; PSORBFNN ; recognition ; Signal processing algorithms ; train wheel ; Training ; tread damage ; Wheels</subject><ispartof>2012 8th International Conference on Natural Computation, 2012, p.1093-1095</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/6234662$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54555,54920,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6234662$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhao Yong</creatorcontrib><creatorcontrib>Ye Hong</creatorcontrib><creatorcontrib>Kang Zheng-sheng</creatorcontrib><creatorcontrib>Shi Song-shan</creatorcontrib><creatorcontrib>Zhou Lin</creatorcontrib><title>The recognition of train wheel tread damages based on PSO-RBFNN algorithm</title><title>2012 8th International Conference on Natural Computation</title><addtitle>ICNC</addtitle><description>In order to the recognition of the train wheel tread damages, the pattern recognition method of the train wheel tread damages based on PSO-RBFNN was developed. The algorithm uses PSO-RBFNN algorithm to optimize center and spread of RBFNN, the connection weight value is sovled by least squares method. Compared with the traditional RBFNN,BP and GA-RBFNN, the experiment results show that the recognition rate of testing samples is higher than the traditional RBFNN, BP and GA-RBFNN, the evolutional generations of PSO-RBFNN algorithm were less than RBFNN, BP and GA-RBFNN.</description><subject>Accuracy</subject><subject>Feature extraction</subject><subject>Inspection</subject><subject>Pattern recognition</subject><subject>PSORBFNN</subject><subject>recognition</subject><subject>Signal processing algorithms</subject><subject>train wheel</subject><subject>Training</subject><subject>tread damage</subject><subject>Wheels</subject><issn>2157-9555</issn><isbn>9781457721304</isbn><isbn>1457721309</isbn><isbn>9781457721328</isbn><isbn>1457721325</isbn><isbn>9781457721335</isbn><isbn>1457721333</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpVUMFKxDAUjKjgsvYDxEt-oDXvNWnaoxZXC0tXtPclaV_aSLeVtiD-vQX34lzmDQzDvGHsDkQEILKHIi_zCAVglGAskwQvWJDpFKTSGiHG9PKfFvKKbRCUDjOl1A0L5vlTrNAK0kRuWFF1xCeqx3bwix8HPjq-TMYP_Lsj6tebTMMbczItzdyamRq-ut4-DuH7064suenbcfJLd7pl1870MwVn3rJq91zlr-H-8FLkj_vQZ2IJUaRWN-sjUrnEoZINZSCtcZYUptrRSoC1bRKoUanUEpBRNVpHa2HAeMvu_2I9ER2_Jn8y08_xvEX8C8V6TxE</recordid><startdate>201205</startdate><enddate>201205</enddate><creator>Zhao Yong</creator><creator>Ye Hong</creator><creator>Kang Zheng-sheng</creator><creator>Shi Song-shan</creator><creator>Zhou Lin</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201205</creationdate><title>The recognition of train wheel tread damages based on PSO-RBFNN algorithm</title><author>Zhao Yong ; Ye Hong ; Kang Zheng-sheng ; Shi Song-shan ; Zhou Lin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-208b7d10945f6f254de914bafbe5287fee5212cbd61c2558be1ea5c2bfe186123</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Accuracy</topic><topic>Feature extraction</topic><topic>Inspection</topic><topic>Pattern recognition</topic><topic>PSORBFNN</topic><topic>recognition</topic><topic>Signal processing algorithms</topic><topic>train wheel</topic><topic>Training</topic><topic>tread damage</topic><topic>Wheels</topic><toplevel>online_resources</toplevel><creatorcontrib>Zhao Yong</creatorcontrib><creatorcontrib>Ye Hong</creatorcontrib><creatorcontrib>Kang Zheng-sheng</creatorcontrib><creatorcontrib>Shi Song-shan</creatorcontrib><creatorcontrib>Zhou Lin</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>IEEE Xplore</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>Zhao Yong</au><au>Ye Hong</au><au>Kang Zheng-sheng</au><au>Shi Song-shan</au><au>Zhou Lin</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>The recognition of train wheel tread damages based on PSO-RBFNN algorithm</atitle><btitle>2012 8th International Conference on Natural Computation</btitle><stitle>ICNC</stitle><date>2012-05</date><risdate>2012</risdate><spage>1093</spage><epage>1095</epage><pages>1093-1095</pages><issn>2157-9555</issn><isbn>9781457721304</isbn><isbn>1457721309</isbn><eisbn>9781457721328</eisbn><eisbn>1457721325</eisbn><eisbn>9781457721335</eisbn><eisbn>1457721333</eisbn><abstract>In order to the recognition of the train wheel tread damages, the pattern recognition method of the train wheel tread damages based on PSO-RBFNN was developed. The algorithm uses PSO-RBFNN algorithm to optimize center and spread of RBFNN, the connection weight value is sovled by least squares method. Compared with the traditional RBFNN,BP and GA-RBFNN, the experiment results show that the recognition rate of testing samples is higher than the traditional RBFNN, BP and GA-RBFNN, the evolutional generations of PSO-RBFNN algorithm were less than RBFNN, BP and GA-RBFNN.</abstract><pub>IEEE</pub><doi>10.1109/ICNC.2012.6234662</doi><tpages>3</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 2157-9555
ispartof 2012 8th International Conference on Natural Computation, 2012, p.1093-1095
issn 2157-9555
language eng
recordid cdi_ieee_primary_6234662
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Accuracy
Feature extraction
Inspection
Pattern recognition
PSORBFNN
recognition
Signal processing algorithms
train wheel
Training
tread damage
Wheels
title The recognition of train wheel tread damages based on PSO-RBFNN algorithm
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T03%3A09%3A49IST&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=The%20recognition%20of%20train%20wheel%20tread%20damages%20based%20on%20PSO-RBFNN%20algorithm&rft.btitle=2012%208th%20International%20Conference%20on%20Natural%20Computation&rft.au=Zhao%20Yong&rft.date=2012-05&rft.spage=1093&rft.epage=1095&rft.pages=1093-1095&rft.issn=2157-9555&rft.isbn=9781457721304&rft.isbn_list=1457721309&rft_id=info:doi/10.1109/ICNC.2012.6234662&rft.eisbn=9781457721328&rft.eisbn_list=1457721325&rft.eisbn_list=9781457721335&rft.eisbn_list=1457721333&rft_dat=%3Cieee_6IE%3E6234662%3C/ieee_6IE%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i90t-208b7d10945f6f254de914bafbe5287fee5212cbd61c2558be1ea5c2bfe186123%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=6234662&rfr_iscdi=true