Abstract
Linear-in-parameters models are quite widespread in
process engineering, e.g., nonlinear additive
autoregressive models, polynomial ARMA models, etc.
This paper proposes a new method for the structure
selection of these models. The method uses genetic
programming to generate nonlinear input-output models
of dynamical systems that are represented in a tree
structure. The main idea of the paper is to apply the
orthogonal least squares (OLS) algorithm to estimate
the contribution of the branches of the tree to the
accuracy of the model. This method results in more
robust and interpretable models. The proposed approach
has been implemented as a freely available MATLAB
Toolbox, www.fmt.veim.hu/softcomp. The simulation
results show that the developed tool provides an
efficient and fast method for determining the order and
structure for nonlinear input-output models.
Users
Please
log in to take part in the discussion (add own reviews or comments).