An Evolutionary Algorithm for Linear Systems Identification

This paper presents a systems identification method, for discrete time linear systems, based on an evolutionary approach, which allows achieving the selection of a suitable structure and the parameters estimation, using non conventional objective functions. This algorithm incorporates parametric crossover and parametric mutation along a weighted gradient direction [1]. The performance of the proposed method is illustrated with computer simulations using ARX model structures, where parameters, model dynamical order and input-output delay values are estimated. Key-Words: System identification, Discrete time systems, Regression models, Evolutionary computation.