Application of artificial neural networks in linear profile monitoring
► Three ANN based methods for monitoring linear profiles are proposed. ► Proposed methods perform better than T2, EWMA/R and EWMA-3 charts for medium to large shifts. ► A advantage of proposed ANN methods is to train the neural networks to detect desired shifts. ► ARL criterion is used to assess the...
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| Veröffentlicht in: | Expert systems with applications 2011-05, Vol.38 (5), p.4920-4928 |
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| creator | Hosseinifard, S.Z. Abdollahian, M. Zeephongsekul, P. |
| description | ► Three ANN based methods for monitoring linear profiles are proposed. ► Proposed methods perform better than T2, EWMA/R and EWMA-3 charts for medium to large shifts. ► A advantage of proposed ANN methods is to train the neural networks to detect desired shifts. ► ARL criterion is used to assess the efficiencies of the methods. ► Control charts are used to detect shifts in intercept, slope and residuals.
In many quality control applications the quality of process or product is characterized and summarized by a relation (profile) between a response variable and one or more explanatory variables. Such profiles can be modeled using linear or nonlinear regression models. In this paper we use artificial neural networks to detect and classify the shifts in linear profiles. Three monitoring methods based on artificial neural networks are developed to monitor linear profiles. Their efficacies are assessed using average run length criterion. |
| doi_str_mv | 10.1016/j.eswa.2010.09.160 |
| format | Article |
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In many quality control applications the quality of process or product is characterized and summarized by a relation (profile) between a response variable and one or more explanatory variables. Such profiles can be modeled using linear or nonlinear regression models. In this paper we use artificial neural networks to detect and classify the shifts in linear profiles. Three monitoring methods based on artificial neural networks are developed to monitor linear profiles. Their efficacies are assessed using average run length criterion.</description><identifier>ISSN: 0957-4174</identifier><identifier>EISSN: 1873-6793</identifier><identifier>DOI: 10.1016/j.eswa.2010.09.160</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Artificial neural networks ; Classification ; Effectiveness ; Expert systems ; Linear profile ; Linear regression ; Mathematical models ; Monitoring ; Monitors ; Neural networks ; Regression ; Statistical quality control</subject><ispartof>Expert systems with applications, 2011-05, Vol.38 (5), p.4920-4928</ispartof><rights>2010 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c365t-a1b840cd017ca80ab983b0236d73ffa2327c42cb8875b1ba6b65bb598f2eff113</citedby><cites>FETCH-LOGICAL-c365t-a1b840cd017ca80ab983b0236d73ffa2327c42cb8875b1ba6b65bb598f2eff113</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0957417410011176$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65534</link.rule.ids></links><search><creatorcontrib>Hosseinifard, S.Z.</creatorcontrib><creatorcontrib>Abdollahian, M.</creatorcontrib><creatorcontrib>Zeephongsekul, P.</creatorcontrib><title>Application of artificial neural networks in linear profile monitoring</title><title>Expert systems with applications</title><description>► Three ANN based methods for monitoring linear profiles are proposed. ► Proposed methods perform better than T2, EWMA/R and EWMA-3 charts for medium to large shifts. ► A advantage of proposed ANN methods is to train the neural networks to detect desired shifts. ► ARL criterion is used to assess the efficiencies of the methods. ► Control charts are used to detect shifts in intercept, slope and residuals.
In many quality control applications the quality of process or product is characterized and summarized by a relation (profile) between a response variable and one or more explanatory variables. Such profiles can be modeled using linear or nonlinear regression models. In this paper we use artificial neural networks to detect and classify the shifts in linear profiles. Three monitoring methods based on artificial neural networks are developed to monitor linear profiles. Their efficacies are assessed using average run length criterion.</description><subject>Artificial neural networks</subject><subject>Classification</subject><subject>Effectiveness</subject><subject>Expert systems</subject><subject>Linear profile</subject><subject>Linear regression</subject><subject>Mathematical models</subject><subject>Monitoring</subject><subject>Monitors</subject><subject>Neural networks</subject><subject>Regression</subject><subject>Statistical quality control</subject><issn>0957-4174</issn><issn>1873-6793</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNp9kDFPwzAQhS0EEqXwB5iywZJwtmM7kViqigJSJRaYLduxkUsaBzsF8e9xKXOnk07fu3fvIXSNocKA-d2msulbVQTyAtoKczhBM9wIWnLR0lM0g5aJssaiPkcXKW0AsAAQM7RajGPvjZp8GIrgChUn77zxqi8Gu4t_Y_oO8SMVfih6P1gVizEG53tbbMPgpxD98H6Jzpzqk736n3P0tnp4XT6V65fH5-ViXRrK2VQqrJsaTJfdjWpA6bahGgjlnaDOKUKJMDUxumkE01grrjnTmrWNI9Y5jOkc3Rzu5hc-dzZNcuuTsX2vBht2STa8pi1jRGTy9iiZ82MAlo0ySg6oiSGlaJ0co9-q-CMxyH29ciP39cp9vRJamevNovuDyOa4X95GmYy3g7Gdj9ZMsgv-mPwXsR-ECA</recordid><startdate>201105</startdate><enddate>201105</enddate><creator>Hosseinifard, S.Z.</creator><creator>Abdollahian, M.</creator><creator>Zeephongsekul, P.</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201105</creationdate><title>Application of artificial neural networks in linear profile monitoring</title><author>Hosseinifard, S.Z. ; Abdollahian, M. ; Zeephongsekul, P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c365t-a1b840cd017ca80ab983b0236d73ffa2327c42cb8875b1ba6b65bb598f2eff113</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Artificial neural networks</topic><topic>Classification</topic><topic>Effectiveness</topic><topic>Expert systems</topic><topic>Linear profile</topic><topic>Linear regression</topic><topic>Mathematical models</topic><topic>Monitoring</topic><topic>Monitors</topic><topic>Neural networks</topic><topic>Regression</topic><topic>Statistical quality control</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hosseinifard, S.Z.</creatorcontrib><creatorcontrib>Abdollahian, M.</creatorcontrib><creatorcontrib>Zeephongsekul, P.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Expert systems with applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hosseinifard, S.Z.</au><au>Abdollahian, M.</au><au>Zeephongsekul, P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of artificial neural networks in linear profile monitoring</atitle><jtitle>Expert systems with applications</jtitle><date>2011-05</date><risdate>2011</risdate><volume>38</volume><issue>5</issue><spage>4920</spage><epage>4928</epage><pages>4920-4928</pages><issn>0957-4174</issn><eissn>1873-6793</eissn><abstract>► Three ANN based methods for monitoring linear profiles are proposed. ► Proposed methods perform better than T2, EWMA/R and EWMA-3 charts for medium to large shifts. ► A advantage of proposed ANN methods is to train the neural networks to detect desired shifts. ► ARL criterion is used to assess the efficiencies of the methods. ► Control charts are used to detect shifts in intercept, slope and residuals.
In many quality control applications the quality of process or product is characterized and summarized by a relation (profile) between a response variable and one or more explanatory variables. Such profiles can be modeled using linear or nonlinear regression models. In this paper we use artificial neural networks to detect and classify the shifts in linear profiles. Three monitoring methods based on artificial neural networks are developed to monitor linear profiles. Their efficacies are assessed using average run length criterion.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.eswa.2010.09.160</doi><tpages>9</tpages></addata></record> |
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| language | eng |
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| source | Elsevier ScienceDirect Journals Complete |
| subjects | Artificial neural networks Classification Effectiveness Expert systems Linear profile Linear regression Mathematical models Monitoring Monitors Neural networks Regression Statistical quality control |
| title | Application of artificial neural networks in linear profile monitoring |
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