Comparison of different subset selection algorithms for learning local model networks with higher degree polynomials

Abstract

A comparison of three different subset selection methods in combination with a new learning algorithm for nonlinear system identification with local models of higher polynomial degree is presented in this paper. Usually the local models are linearly parameterized and those parameters are typically estimated by some least squares approach. For the utilization of higher degree polynomials this procedure is no longer feasible since the amount of parameters grows rapidly with the number of physical inputs and the polynomial degree. Thus a new learning strategy with the aid of subset selection methods is developed to estimate only the most significant parameters. A forward selection method with orthogonal least squares, a stepwise regression and a least angle regression method are used for training different neural networks. A comparison of the trained networks shows the benefits of each subset selection method.