Variable Selection in Nonlinear Systems Modelling

A new algorithm which preselects variables in nonlinear system models is introduced by converting the problem into a variable selection procedure for a set of linearised models. Based on this result an algorithm which consists of a cluster analysis linearisation sub-region division procedure, a linear subset selection routine usin an all possible regression algorithm and a genetic algorithm is developed. This algorithm can be applied to the modelling of nonlinear systems using a wide class of model forms including the nonlinear polynomial model, the nonlinear rational model, artificial neural networks and others. Numerical simulations are included to demonstrate the efficiency of the new algorithm.