A population based algorithm and fuzzy decision trees for nonlinear modeling

Abstract

The paper presents a new approach for using the fuzzy decision trees for non-linear modeling based on the capabilities of participle swarm optimization and evolutionary algorithms. The most nonlinear dynamic objects have their approximate nonlinear model. Their parameters are known or can be determined by one of the typical identification procedure. The obtained approximate nonlinear model describes well the identified dynamic object only in the operating point. In this work, we use hybrid model composed with of two parts: approximate nonlinear model and fuzzy decision tree. The fuzzy decision tree contains correction values of the parameters in terminal nodes. The hybrid model ensures sufficient accuracy for the practical applications. A participle swarm optimization and evolutionary algorithm were used for identification of the parameters of the approximate nonlinear model and fuzzy decision tree. An important benefit of the proposed method is the obtained characteristics of the unknown parameters of the approximate nonlinear model described by the terminal nodes of the fuzzy decision tree. They present valuable and interpretable knowledge for the experts concerning the essence of the unknown phenomena.