Nonlinear Structure Identification with Linear Least Squares and ANOVA
Abstract
The objective of this paper is to find the structure of a nonlinear system frommeasurement data, as a prior step to model estimation. Applying ANOVAdirectly on a dataset is compared to applying ANOVA on residualsfrom a linear model. The distributions of the involved test variables are computed and usedto show that ANOVA is effective in finding what regressors give lineareffects and what regressors produce nonlinear effects. The ability tofind nonlinear substructures depending on only subsets of regressors is an ANOVA feature which is shown not to be affected by subtracting a linear model.
