Show simple item record

dc.contributor.authorWang, Z
dc.contributor.authorUlanowski, Z
dc.contributor.authorKaye, Paul H.
dc.date.accessioned2011-12-21T11:01:13Z
dc.date.available2011-12-21T11:01:13Z
dc.date.issued1999
dc.identifier.citationWang , Z , Ulanowski , Z & Kaye , P H 1999 , ' On solving the inverse scattering problem with RBF neural networks : Noise-free case ' , Neural Computing and Applications , vol. 8 , no. 2 , pp. 177-186 . https://doi.org/10.1007/s005210050019
dc.identifier.issn0941-0643
dc.identifier.otherPURE: 418779
dc.identifier.otherPURE UUID: aac07f4a-4499-43ac-bc1b-0057b8e945d4
dc.identifier.otherWOS: 000081066400009
dc.identifier.otherScopus: 0033244194
dc.identifier.urihttp://hdl.handle.net/2299/7457
dc.description“The original publication is available at www.springerlink.com” Copyright Springer
dc.description.abstractNeural networks are successfully used to determine small particle properties from knowledge of the scattered light - an inverse light scattering problem. This type of problem is inherently difficult to solve as it is represented by a highly Ill-posed function mapping. This paper presents a technique that solves the inverse light scattering problem for spheres using Radial Basis Function (RBF) neural networks. A two-stage network architecture is arranged to enhance network approximation capability. In addition, a new approach to computing basis function parameters with respect to the inverse scattering problem is demonstrated The technique is evaluated for noise-free data through simulations, in which a minimum 99.06% approximation accuracy is achieved. A comparison is made between the least square and the orthogonal least square training methods.en
dc.format.extent10
dc.language.isoeng
dc.relation.ispartofNeural Computing and Applications
dc.subjectbasis function widths
dc.subjectfunction approximation
dc.subjectinverse light scattering problem
dc.subjectRadial Basis Function neural networks
dc.subjectsmall particles
dc.titleOn solving the inverse scattering problem with RBF neural networks : Noise-free caseen
dc.contributor.institutionSchool of Physics, Astronomy and Mathematics
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionLight Scattering & Radiactive Properties
dc.contributor.institutionCentre for Atmospheric and Climate Physics Research
dc.contributor.institutionParticle Instrumentation
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Physics, Astronomy and Mathematics
dcterms.dateAccepted1999
rioxxterms.versionofrecordhttps://doi.org/10.1007/s005210050019
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record