Material structure-property prediction using orthogonal functional basis neural network

Status	已發表Published
	Material structure-property prediction using orthogonal functional basis neural network
	Chen C.L.Philip; Cao Yang; LeClair Steven R.
	1997-12-01
Source Publication	Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume	3
Pages	2521-2526
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 here demonstrate the performance, behavior, and advantages of the proposed network.
URL	View the original
Language	英語English
Fulltext Access	View Full-Text via Scopus
Document Type	Conference paper
Collection	University of Macau
Affiliation	Wright-Patterson AFB
Recommended Citation GB/T 7714	Chen C.L.Philip,Cao Yang,LeClair Steven R.. Material structure-property prediction using orthogonal functional basis neural network[C], 1997, 2521-2526.
APA	Chen C.L.Philip., Cao Yang., & LeClair Steven R. (1997). Material structure-property prediction using orthogonal functional basis neural network. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 3, 2521-2526.

Files in This Item:
There are no files associated with this item.

If you have any objections to this item, please fill out the form below and the administrator will contact you as soon as possible.
Content:
Email：	*
Affiliation No.
Verification Code:	Refresh

Any comments and suggestions are welcomed.
Title:	*
Content:
Email：	*
Verification Code:	Refresh