Online nonlinear system identification using linear model trees

Authors:	Potts Duncan, University of New South Wales, Australia Sammut Claude, University of New South Wales, Australia
Topic:	1.1 Modelling, Identification & Signal Processing
Session:	Nonlinear System Identification I
Keywords:	System Identification, Nonlinear Models, Non-parametric Regression, Decision Trees.

Abstract

A linear model tree is a decision tree with a linear functional model in each leaf that can represent piecewise linear systems. This paper extends recent work in the machine learning literature on the online learning of linear model trees. Both the leaf splitting rule and the pruning rule are improved to take into account more information. The algorithm is demonstrated on two nonlinear system identification problems, including the modelling of complex high dimensional flight dynamics. Results compare favourably with existing online pproaches, including an alternative linear model tree algorithm.