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.
|Publication status||Published - 22 Nov 1991|
|Event|| IEE Colloquium on Adaptive Filtering, Non-Linear Dynamics and Neural Networks - London, United Kingdom|
Duration: 22 Nov 1991 → 22 Nov 1991
|Conference||IEE Colloquium on Adaptive Filtering, Non-Linear Dynamics and Neural Networks|
|Period||22/11/91 → 22/11/91|