A type I structure identification approach using feedforward neural networks

System identification can be divided into structure identification and parameter identification. In most system identification approaches the structure is presumed and only a parameter identification is performed to obtain the coefficients in the functional system. Unfortunately in many cases there is little knowledge about the system structure. The structure identification itself can be divided into two types: the identification of the input variables of the model and the input-output relation, here respectively named structure identification type I and type II. In this paper a black-box structure identification type I approach, using a feedforward neural network in combination with the regularity criterion in GMDH (group method of data handling) and a novel identification algorithm, is proposed