Vector subspaces in nonlinear prediction

Kenneth Nisbet, Steven McLaughlin, Bernard Mulgrew

Research output: Contribution to conferencePaper

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.
Original languageEnglish
Publication statusPublished - 22 Nov 1991
Event IEE Colloquium on Adaptive Filtering, Non-Linear Dynamics and Neural Networks - London, United Kingdom
Duration: 22 Nov 199122 Nov 1991

Conference

Conference IEE Colloquium on Adaptive Filtering, Non-Linear Dynamics and Neural Networks
Country/TerritoryUnited Kingdom
CityLondon
Period22/11/9122/11/91

Fingerprint

Dive into the research topics of 'Vector subspaces in nonlinear prediction'. Together they form a unique fingerprint.

Cite this