Abstract
A nonlinear black-box structure for a dynamic system is a model structure that is prepared to describe virtually any nonlinear dynamics. The majority of nonlinear models based on neural networks are of the black-box structure. A nonlinear system can be nonlinear in many different ways, thus the nonlinear black-box model structure must be very flexible. This means that it must have many parameters. A model offering many parameters usually creates problems, and the variance contribution to the error might be high. For a particular identification problem, only a subset of the parameters might be necessary and the main topic in nonlinear system identification is how to select a model structure that describes the system dynamics with the minimum number of parameters. This paper discusses nonlinear input-output models that are suitable for implementation of feed-forward neural networks. The proposed model structures were tested and compared using the identification procedure of a pH process. The results indicated that it would be worthwhile using the simplest model structure that can satisfactorily represent the investigated process.