Title :
Vector subspaces in non-linear prediction
Author :
Mulgrew, B. ; Nisbet, K. ; McLaughlin, S.
Author_Institution :
Dept. of Electr. Eng., Edinburgh Univ., UK
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;
Conference_Titel :
Adaptive Filtering, Non-Linear Dynamics and Neural Networks, IEE Colloquium on
Conference_Location :
London
