Construction of composite models from large data-sets

Based on input-output measurements and measurements of the operating-point vector a composite model is constructed. The dynamics of the different linear models are determined from the data, as well as the boundaries in the operating-point space which determine the dependence of the dynamics on the operating point. The basic idea is to utilize a method for recursive identification that is able to track slow as well as rapid dynamic changes. A classification procedure is applied to the models produced by this identification procedure, and borders are created between the different classified models. Techniques for supervised pattern recognition are used for the latter step. The whole construction procedure is illustrated by an example.<>