A proposed life cycle forecasting model of complex recycling technical systems by implementing neural super network
The purpose of this paper is to increase the efficiency of functionality and reliability of complex recycling technical systems (CRTS) community, through improving the control quality of their life cycle. Automated control system (ACS) on the basis of neural super-network learning for forecasting da...
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| creator | Yaghi, K.A. Abu-Dawwas, W.A. |
| description | The purpose of this paper is to increase the efficiency of functionality and reliability of complex recycling technical systems (CRTS) community, through improving the control quality of their life cycle. Automated control system (ACS) on the basis of neural super-network learning for forecasting damages and ensuring its information representation for learning was proposed. In the paper it was suggested an architecture and a method of learning of a neural super-network for forecasting the progress of CRTS community life cycle. |
| doi_str_mv | 10.1109/NDT.2009.5272222 |
| format | Conference Proceeding |
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Automated control system (ACS) on the basis of neural super-network learning for forecasting damages and ensuring its information representation for learning was proposed. 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In the paper it was suggested an architecture and a method of learning of a neural super-network for forecasting the progress of CRTS community life cycle.</description><subject>Artificial neural networks</subject><subject>Automated Control System (ACS)</subject><subject>Automatic control</subject><subject>Cathode ray tubes</subject><subject>Complex Recycling Technical Systems (CRTS)</subject><subject>Control systems</subject><subject>Information representation</subject><subject>Management information systems</subject><subject>Neural Network (NN)</subject><subject>Neural networks</subject><subject>Neural Super-Network</subject><subject>Optical computing</subject><subject>Predictive models</subject><subject>Recycling</subject><issn>2155-8728</issn><isbn>1424446147</isbn><isbn>9781424446148</isbn><isbn>1424446155</isbn><isbn>9781424446155</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkEtPwzAQhI2gEm3pHYmL_0CKH-s4PlaFAlIFl3KuHHcDhrxkp4L8exKoxF5Ws9_MHJaQa86WnDNz-3y3WwrGzFIJLYY5IzMOAgBSrtT5vwB9QaZiuCWZFtmEzMaQYSBSdkkWMX4wxqRgElI5JXFF29C0TcQDLX2B1PWuRFo0AZ2Nna_faNUcsKRNQV1TtSV-0wENphF16N5r72xJYx87rCLNe-pHV4X1b7jGYxjxscUwiO6rCZ9XZFLYMuLitOfkdXO_Wz8m25eHp_Vqm3iuVZc4mdqMpZJDngMYmXOdWYAC0VolnXWcgQIljNEpGo5OHKw2TIF1OSBzck5u_no9Iu7b4Csb-v3pe_IHtMphKA</recordid><startdate>200907</startdate><enddate>200907</enddate><creator>Yaghi, K.A.</creator><creator>Abu-Dawwas, W.A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200907</creationdate><title>A proposed life cycle forecasting model of complex recycling technical systems by implementing neural super network</title><author>Yaghi, K.A. ; Abu-Dawwas, W.A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-c36a806314bb4493b178a44feeaa53cac10454529976e91ec2da79054acb4e0c3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Artificial neural networks</topic><topic>Automated Control System (ACS)</topic><topic>Automatic control</topic><topic>Cathode ray tubes</topic><topic>Complex Recycling Technical Systems (CRTS)</topic><topic>Control systems</topic><topic>Information representation</topic><topic>Management information systems</topic><topic>Neural Network (NN)</topic><topic>Neural networks</topic><topic>Neural Super-Network</topic><topic>Optical computing</topic><topic>Predictive models</topic><topic>Recycling</topic><toplevel>online_resources</toplevel><creatorcontrib>Yaghi, K.A.</creatorcontrib><creatorcontrib>Abu-Dawwas, W.A.</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 Electronic Library (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>Yaghi, K.A.</au><au>Abu-Dawwas, W.A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A proposed life cycle forecasting model of complex recycling technical systems by implementing neural super network</atitle><btitle>2009 First International Conference on Networked Digital Technologies</btitle><stitle>NDT</stitle><date>2009-07</date><risdate>2009</risdate><spage>482</spage><epage>484</epage><pages>482-484</pages><issn>2155-8728</issn><isbn>1424446147</isbn><isbn>9781424446148</isbn><eisbn>1424446155</eisbn><eisbn>9781424446155</eisbn><abstract>The purpose of this paper is to increase the efficiency of functionality and reliability of complex recycling technical systems (CRTS) community, through improving the control quality of their life cycle. Automated control system (ACS) on the basis of neural super-network learning for forecasting damages and ensuring its information representation for learning was proposed. In the paper it was suggested an architecture and a method of learning of a neural super-network for forecasting the progress of CRTS community life cycle.</abstract><pub>IEEE</pub><doi>10.1109/NDT.2009.5272222</doi><tpages>3</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 2155-8728 |
| ispartof | 2009 First International Conference on Networked Digital Technologies, 2009, p.482-484 |
| issn | 2155-8728 |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Automated Control System (ACS) Automatic control Cathode ray tubes Complex Recycling Technical Systems (CRTS) Control systems Information representation Management information systems Neural Network (NN) Neural networks Neural Super-Network Optical computing Predictive models Recycling |
| title | A proposed life cycle forecasting model of complex recycling technical systems by implementing neural super network |
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