DocumentCode
286894
Title
Vector subspaces in non-linear prediction
Author
Mulgrew, B. ; Nisbet, K. ; McLaughlin, S.
Author_Institution
Dept. of Electr. Eng., Edinburgh Univ., UK
fYear
1991
fDate
33564
Firstpage
42401
Lastpage
42406
Abstract
Radial basis function and Volterra series predictors are examined with a view to reducing their complexity while maintaining prediction performance. A geometrical interpretation of the problem is presented. This interpretation indicates that while a multiplicity of choices of reduced state predictor exist, some may be better than others in terms of the numerical conditioning of the solution
Keywords
filtering and prediction theory; numerical methods; series (mathematics); vectors; Volterra series predictors; geometrical interpretation; nonlinear prediction; numerical conditioning; prediction performance; radial basis function series; reduced state predictor; vector subspaces;
fLanguage
English
Publisher
iet
Conference_Titel
Adaptive Filtering, Non-Linear Dynamics and Neural Networks, IEE Colloquium on
Conference_Location
London
Type
conf
Filename
263743
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