Neural network with variable excitability of input units (NN-VEIN)
NN-VEIN is a novel approach to the structure of neural networks. In this structure the input units are located in a network of connections. Traditional NN and NN-VEIN are compared. In this structure, input units are located in weighted connections of which the degree may be variable from 0 to N. Thi...
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| creator | Sayad, S. Sayad, J. Sayad, M.H. |
| description | NN-VEIN is a novel approach to the structure of neural networks. In this structure the input units are located in a network of connections. Traditional NN and NN-VEIN are compared. In this structure, input units are located in weighted connections of which the degree may be variable from 0 to N. This degree varies with respect to the quantity of input data. This network is being used to design intelligent systems in medical diagnosis. The input layer in NN-VEIN in comparison with NN is completely different and is a really intelligent process. Clinical diagnosis expert systems created by the use of this network do not need an input pattern to be entered at once, but like an expert physician, any question is based on previous questions.< > |
| doi_str_mv | 10.1109/PACRIM.1991.160810 |
| format | Conference Proceeding |
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In this structure the input units are located in a network of connections. Traditional NN and NN-VEIN are compared. In this structure, input units are located in weighted connections of which the degree may be variable from 0 to N. This degree varies with respect to the quantity of input data. This network is being used to design intelligent systems in medical diagnosis. The input layer in NN-VEIN in comparison with NN is completely different and is a really intelligent process. Clinical diagnosis expert systems created by the use of this network do not need an input pattern to be entered at once, but like an expert physician, any question is based on previous questions.< ></description><identifier>ISBN: 0879426381</identifier><identifier>ISBN: 9780879426385</identifier><identifier>DOI: 10.1109/PACRIM.1991.160810</identifier><language>eng</language><publisher>IEEE</publisher><subject>Biomedical engineering ; Clinical diagnosis ; Neural networks</subject><ispartof>[1991] IEEE Pacific Rim Conference on Communications, Computers and Signal Processing Conference Proceedings, 1991, p.601-602 vol.2</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/160810$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,4048,4049,27924,54919</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/160810$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Sayad, S.</creatorcontrib><creatorcontrib>Sayad, J.</creatorcontrib><creatorcontrib>Sayad, M.H.</creatorcontrib><title>Neural network with variable excitability of input units (NN-VEIN)</title><title>[1991] IEEE Pacific Rim Conference on Communications, Computers and Signal Processing Conference Proceedings</title><addtitle>PACRIM</addtitle><description>NN-VEIN is a novel approach to the structure of neural networks. In this structure the input units are located in a network of connections. Traditional NN and NN-VEIN are compared. In this structure, input units are located in weighted connections of which the degree may be variable from 0 to N. This degree varies with respect to the quantity of input data. This network is being used to design intelligent systems in medical diagnosis. The input layer in NN-VEIN in comparison with NN is completely different and is a really intelligent process. Clinical diagnosis expert systems created by the use of this network do not need an input pattern to be entered at once, but like an expert physician, any question is based on previous questions.< ></description><subject>Biomedical engineering</subject><subject>Clinical diagnosis</subject><subject>Neural networks</subject><isbn>0879426381</isbn><isbn>9780879426385</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1991</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj0FLwzAYhgMiqHN_YKcc9dD6fUmTJsdZpha2TkS9jqRNMVq7kabO_XsH8z08z-2Bl5AZQooI-u55XryUqxS1xhQlKIQzcgUq1xmTXOEFmQ7DJxyXCcgzuCT3lRuD6Wjv4n4bvujexw_6Y4I3tnPU_dY-Gus7Hw9021Lf78ZIx97Hgd5UVfK-KKvba3Lemm5w039PyNvD4rV4Spbrx7KYLxOPOYuJkcq0oK1SwlnboNBHSMEZR1UDCMWgaRFqxZQVec65ZE1tLTiwhsmm4RMyO3W9c26zC_7bhMPm9JL_AT48R5g</recordid><startdate>1991</startdate><enddate>1991</enddate><creator>Sayad, S.</creator><creator>Sayad, J.</creator><creator>Sayad, M.H.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1991</creationdate><title>Neural network with variable excitability of input units (NN-VEIN)</title><author>Sayad, S. ; Sayad, J. ; Sayad, M.H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i172t-a68af09b885ebbd159bd16532318c005820df10c828b5773362dcbb0e0ba26dd3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1991</creationdate><topic>Biomedical engineering</topic><topic>Clinical diagnosis</topic><topic>Neural networks</topic><toplevel>online_resources</toplevel><creatorcontrib>Sayad, S.</creatorcontrib><creatorcontrib>Sayad, J.</creatorcontrib><creatorcontrib>Sayad, M.H.</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>Sayad, S.</au><au>Sayad, J.</au><au>Sayad, M.H.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Neural network with variable excitability of input units (NN-VEIN)</atitle><btitle>[1991] IEEE Pacific Rim Conference on Communications, Computers and Signal Processing Conference Proceedings</btitle><stitle>PACRIM</stitle><date>1991</date><risdate>1991</risdate><spage>601</spage><epage>602 vol.2</epage><pages>601-602 vol.2</pages><isbn>0879426381</isbn><isbn>9780879426385</isbn><abstract>NN-VEIN is a novel approach to the structure of neural networks. In this structure the input units are located in a network of connections. Traditional NN and NN-VEIN are compared. In this structure, input units are located in weighted connections of which the degree may be variable from 0 to N. This degree varies with respect to the quantity of input data. This network is being used to design intelligent systems in medical diagnosis. The input layer in NN-VEIN in comparison with NN is completely different and is a really intelligent process. Clinical diagnosis expert systems created by the use of this network do not need an input pattern to be entered at once, but like an expert physician, any question is based on previous questions.< ></abstract><pub>IEEE</pub><doi>10.1109/PACRIM.1991.160810</doi></addata></record> |
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| identifier | ISBN: 0879426381 |
| ispartof | [1991] IEEE Pacific Rim Conference on Communications, Computers and Signal Processing Conference Proceedings, 1991, p.601-602 vol.2 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Biomedical engineering Clinical diagnosis Neural networks |
| title | Neural network with variable excitability of input units (NN-VEIN) |
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