Material structure-property prediction using orthogonal functional basis neural network
An important trend in materials research is to predict properties for a new material. Often the prediction is motivated by the search for a material with several important materials property features. The selection of the property features is crucial to the plausibility of the prediction. This paper...
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| creator | Chen, C.L.P. Yang Cao LeClair, S.R. |
| description | An important trend in materials research is to predict properties for a new material. Often the prediction is motivated by the search for a material with several important materials property features. The selection of the property features is crucial to the plausibility of the prediction. This paper proposes a neural-network computing approach to evaluate this issue. With the proposed approach, we are able to predict property features for an unknown compound. In this paper we summarize the prediction attained with the proposed neural network structure-orthogonal functional basis neural network (OFBNN). The network, which combines a new basis selection process and a regularization technique, not only gives us a more computationally tractable method, but better generalization performance. Simulation studies presented demonstrate the performance, behavior, and advantages of the proposed network. |
| doi_str_mv | 10.1109/ICSMC.1997.635313 |
| format | Conference Proceeding |
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Often the prediction is motivated by the search for a material with several important materials property features. The selection of the property features is crucial to the plausibility of the prediction. This paper proposes a neural-network computing approach to evaluate this issue. With the proposed approach, we are able to predict property features for an unknown compound. In this paper we summarize the prediction attained with the proposed neural network structure-orthogonal functional basis neural network (OFBNN). The network, which combines a new basis selection process and a regularization technique, not only gives us a more computationally tractable method, but better generalization performance. Simulation studies presented demonstrate the performance, behavior, and advantages of the proposed network.</description><identifier>ISSN: 1062-922X</identifier><identifier>ISBN: 0780340531</identifier><identifier>ISBN: 9780780340534</identifier><identifier>EISSN: 2577-1655</identifier><identifier>DOI: 10.1109/ICSMC.1997.635313</identifier><language>eng</language><publisher>IEEE</publisher><subject>Approximation error ; Diversity reception ; Function approximation ; Least squares methods ; Neural networks ; Radial basis function networks ; Signal mapping ; Unsupervised learning ; Vectors ; Working environment noise</subject><ispartof>1997 IEEE International Conference on Systems, Man, and Cybernetics. 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Computational Cybernetics and Simulation</title><addtitle>ICSMC</addtitle><description>An important trend in materials research is to predict properties for a new material. Often the prediction is motivated by the search for a material with several important materials property features. The selection of the property features is crucial to the plausibility of the prediction. This paper proposes a neural-network computing approach to evaluate this issue. With the proposed approach, we are able to predict property features for an unknown compound. In this paper we summarize the prediction attained with the proposed neural network structure-orthogonal functional basis neural network (OFBNN). The network, which combines a new basis selection process and a regularization technique, not only gives us a more computationally tractable method, but better generalization performance. Simulation studies presented demonstrate the performance, behavior, and advantages of the proposed network.</description><subject>Approximation error</subject><subject>Diversity reception</subject><subject>Function approximation</subject><subject>Least squares methods</subject><subject>Neural networks</subject><subject>Radial basis function networks</subject><subject>Signal mapping</subject><subject>Unsupervised learning</subject><subject>Vectors</subject><subject>Working environment noise</subject><issn>1062-922X</issn><issn>2577-1655</issn><isbn>0780340531</isbn><isbn>9780780340534</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1997</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNp9jsFuwjAQRFelSITCB8DJP5B0bZOEnKMiOHBqpfaGXLqAW2pHa1uIv29Ueu5p3ujNYQBmEgspsXnctM_btpBNUxeVLrXUd5Cpsq5zWZXlAMZYL1EvsDf3kEmsVN4o9TaCcQifiAoXcpnB69ZEYmvOIkRO-5iY8o59RxyvomP6sPtovRMpWHcUnuPJH73r54fkfk2P7ybYIBwl7oujePH8NYHhwZwDTf_yAearp5d2nVsi2nVsvw1fd7ff-l_5AzP8RfE</recordid><startdate>1997</startdate><enddate>1997</enddate><creator>Chen, C.L.P.</creator><creator>Yang Cao</creator><creator>LeClair, S.R.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1997</creationdate><title>Material structure-property prediction using orthogonal functional basis neural network</title><author>Chen, C.L.P. ; Yang Cao ; LeClair, S.R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_6353133</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1997</creationdate><topic>Approximation error</topic><topic>Diversity reception</topic><topic>Function approximation</topic><topic>Least squares methods</topic><topic>Neural networks</topic><topic>Radial basis function networks</topic><topic>Signal mapping</topic><topic>Unsupervised learning</topic><topic>Vectors</topic><topic>Working environment noise</topic><toplevel>online_resources</toplevel><creatorcontrib>Chen, C.L.P.</creatorcontrib><creatorcontrib>Yang Cao</creatorcontrib><creatorcontrib>LeClair, S.R.</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>Chen, C.L.P.</au><au>Yang Cao</au><au>LeClair, S.R.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Material structure-property prediction using orthogonal functional basis neural network</atitle><btitle>1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation</btitle><stitle>ICSMC</stitle><date>1997</date><risdate>1997</risdate><volume>3</volume><spage>2521</spage><epage>2526 vol.3</epage><pages>2521-2526 vol.3</pages><issn>1062-922X</issn><eissn>2577-1655</eissn><isbn>0780340531</isbn><isbn>9780780340534</isbn><abstract>An important trend in materials research is to predict properties for a new material. Often the prediction is motivated by the search for a material with several important materials property features. The selection of the property features is crucial to the plausibility of the prediction. This paper proposes a neural-network computing approach to evaluate this issue. With the proposed approach, we are able to predict property features for an unknown compound. In this paper we summarize the prediction attained with the proposed neural network structure-orthogonal functional basis neural network (OFBNN). The network, which combines a new basis selection process and a regularization technique, not only gives us a more computationally tractable method, but better generalization performance. Simulation studies presented demonstrate the performance, behavior, and advantages of the proposed network.</abstract><pub>IEEE</pub><doi>10.1109/ICSMC.1997.635313</doi></addata></record> |
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| ispartof | 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, 1997, Vol.3, p.2521-2526 vol.3 |
| issn | 1062-922X 2577-1655 |
| language | eng |
| recordid | cdi_ieee_primary_635313 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Approximation error Diversity reception Function approximation Least squares methods Neural networks Radial basis function networks Signal mapping Unsupervised learning Vectors Working environment noise |
| title | Material structure-property prediction using orthogonal functional basis neural network |
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