Overlapped multi-neural-network: a case study
Presents a case study for the overlapped multi-neural-network (OMNN). An overlapped multi-neural-network, structurally, is the same as an ordinary feedforward neural network, but it is considered as one consisting of several subnets. All subnets have the same input-output units, but some different h...
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| creator | Hu, J. Hirasawa, K. |
| description | Presents a case study for the overlapped multi-neural-network (OMNN). An overlapped multi-neural-network, structurally, is the same as an ordinary feedforward neural network, but it is considered as one consisting of several subnets. All subnets have the same input-output units, but some different hidden units. Input-output spaces are partitioned into several parts, each of which corresponds to one subnet of OMNN. Numerical simulations show that such an OMNN has superior performance in that it has better presentation ability than an ordinary neural network and better generalization ability than a non-overlapped multi-neural-network. |
| doi_str_mv | 10.1109/IJCNN.2000.857824 |
| format | Conference Proceeding |
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An overlapped multi-neural-network, structurally, is the same as an ordinary feedforward neural network, but it is considered as one consisting of several subnets. All subnets have the same input-output units, but some different hidden units. Input-output spaces are partitioned into several parts, each of which corresponds to one subnet of OMNN. 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Neural Computing: New Challenges and Perspectives for the New Millennium</title><addtitle>IJCNN</addtitle><description>Presents a case study for the overlapped multi-neural-network (OMNN). An overlapped multi-neural-network, structurally, is the same as an ordinary feedforward neural network, but it is considered as one consisting of several subnets. All subnets have the same input-output units, but some different hidden units. Input-output spaces are partitioned into several parts, each of which corresponds to one subnet of OMNN. Numerical simulations show that such an OMNN has superior performance in that it has better presentation ability than an ordinary neural network and better generalization ability than a non-overlapped multi-neural-network.</description><subject>Computer aided software engineering</subject><subject>Feedforward neural networks</subject><subject>Multi-layer neural network</subject><subject>Neural networks</subject><subject>Numerical simulation</subject><subject>Partitioning algorithms</subject><subject>Pattern recognition</subject><subject>Self organizing feature maps</subject><subject>System identification</subject><subject>Systems engineering and theory</subject><issn>1098-7576</issn><issn>1558-3902</issn><isbn>9780769506197</isbn><isbn>0769506194</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2000</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj8lOwzAQhi0WiarkAeCUF3Dw2PEy3FDEUlS1FzhXE2cqBVKInATUtydSOX2HX_8mxA2oAkDh3eq12mwKrZQqgvVBl2diAdYGaVDpc5GhD8o7tMoB-otZUxikt95diWwYPmYfKGOdhoWQ2x9OHfU9N_lh6sZWfvGUqJsx_n6nz_uc8kgD58M4NcdrcbmnbuDsn0vx_vT4Vr3I9fZ5VT2sZQtejzJ6F0rHvo4GXFkzz7McE2JJWKOHGuNcHjUQGU3Ace8RtWoAORA1xizF7Sm3ZeZdn9oDpePu9NX8AR-VRRY</recordid><startdate>2000</startdate><enddate>2000</enddate><creator>Hu, J.</creator><creator>Hirasawa, K.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>2000</creationdate><title>Overlapped multi-neural-network: a case study</title><author>Hu, J. ; Hirasawa, K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i172t-c76846e7bc3164bee9026ea994a9b971b9c621c21aa32a1ecf79920d19e8aad33</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2000</creationdate><topic>Computer aided software engineering</topic><topic>Feedforward neural networks</topic><topic>Multi-layer neural network</topic><topic>Neural networks</topic><topic>Numerical simulation</topic><topic>Partitioning algorithms</topic><topic>Pattern recognition</topic><topic>Self organizing feature maps</topic><topic>System identification</topic><topic>Systems engineering and theory</topic><toplevel>online_resources</toplevel><creatorcontrib>Hu, J.</creatorcontrib><creatorcontrib>Hirasawa, K.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hu, J.</au><au>Hirasawa, K.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Overlapped multi-neural-network: a case study</atitle><btitle>Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium</btitle><stitle>IJCNN</stitle><date>2000</date><risdate>2000</risdate><volume>1</volume><spage>120</spage><epage>125 vol.1</epage><pages>120-125 vol.1</pages><issn>1098-7576</issn><eissn>1558-3902</eissn><isbn>9780769506197</isbn><isbn>0769506194</isbn><abstract>Presents a case study for the overlapped multi-neural-network (OMNN). An overlapped multi-neural-network, structurally, is the same as an ordinary feedforward neural network, but it is considered as one consisting of several subnets. All subnets have the same input-output units, but some different hidden units. Input-output spaces are partitioned into several parts, each of which corresponds to one subnet of OMNN. Numerical simulations show that such an OMNN has superior performance in that it has better presentation ability than an ordinary neural network and better generalization ability than a non-overlapped multi-neural-network.</abstract><pub>IEEE</pub><doi>10.1109/IJCNN.2000.857824</doi></addata></record> |
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| ispartof | Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000, Vol.1, p.120-125 vol.1 |
| issn | 1098-7576 1558-3902 |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Computer aided software engineering Feedforward neural networks Multi-layer neural network Neural networks Numerical simulation Partitioning algorithms Pattern recognition Self organizing feature maps System identification Systems engineering and theory |
| title | Overlapped multi-neural-network: a case study |
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