Using additive expression programming for system identification

Abstract

The system identification is crucially important process, which could develop the mathematical representation of physical system from observed data. In this paper, a new model, called additive expression tree (AET) model is proposed to encode the linear and nonlinear systems. A new structure-based evolutionary algorithm and artificial bee colony (ABC) are used to optimize the architecture and parameters of additive expression tree model, respectively. Experimental results demonstrate that our proposed model and hybrid approach could identify the linear/nonlinear systems effectively.