Evolutionary GMDH-based identification for nonlinear systems

In designing control systems, it is important to make exact mathematical model of the controlled object. In particular, it is difficult to obtain the mathematical model for nonlinear systems. Therefore, lots of design schemes of nonlinear models have been proposed. As one of them, the group method of data handling (GMDH) network has been proposed as a method to represent such systems. It is a kind of multi-layered networks with a structure which is determined through training, and has a feature that the nonlinear dynamics are expressed as a mathematical model. However, since the mathematical model generated by the GMDH network includes several needless terms, they give unsuitable effects on the estimation accuracy. In this paper, a nonlinear model is identified by using a real data set of an atmospheric distillation process system. Finally, the proposed modeling scheme is evaluated on a simulation example.