Imprecise modelling using gradual rulesand its application to the classification of time series
Résumé
This paper prescrits an alternative to precise analytical modelling, by means of imprecise interpolative models. The model specification is based on gradual rules that express constraints that govern the interpolation mechanism. The modelling strategy is applied to the classification of time series. In this context, it is shown that gond recognition performance can be obtained with models that are highly imprecise.