Show simple item record

dc.contributor.authorSong, Q.
dc.contributor.authorKasabov, N
dc.date.accessioned2009-05-27T22:18:49Z
dc.date.available2009-05-27T22:18:49Z
dc.date.copyright2004
dc.date.created2004
dc.date.issued2009-05-27T22:18:49Z
dc.identifier.urihttp://hdl.handle.net/10292/594
dc.description.abstractThis paper introduces an approach of Weighted Data Normalization (WDN) for Radial Basis Function (RBF) type of neural networks. It presents also applications for medical decision support systems. The WDN method optimizes the data normalization ranges for the input variables of the neural network. A steepest descent algorithm (BP) is used for the WDN-RBF learning. The derived weights have the meaning of feature importance and can be used to select a minimum set of variables (features) that can optimize the performance of the RBF network model. The WDN-RBF is illustrated on two case study prediction/identification problems. The first one is prediction of the Mackey-Glass time series and the second one is a real medical decision support problem of estimating the level of renal functions in patients. The method can be applied to other distance-based, prototype learning neural network models.
dc.publisherIEEE
dc.relation.urihttp://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1380940&isnumber=30107
dc.rights©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.sourceIEEE International Conference on Neural Networks, 3, 2095-2098
dc.titleWDN-RBF: weighted data normalization for radial basic function type neural networks
dc.typeConference Proceedings
dc.rights.accessrightsOpenAccess


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record