Employing correlation clustering for the identification of piecewise affine models

To analyze and control a system, a model is build which describes the relationship between the inputs and the corresponding outputs. While simple systems can be described by a single linear model, more complex systems can be approximated through an assembly of linear submodels. Such piecewise affine (PWA) models consists of several convex regions and linear submodels describing the input output relationship for each such region. The more regions are considered in the PWA model, the more accurate it describes the system. Still, in real world applications, simple models are necessary for performance reasons, hence a trade-off has to be made between the model complexity and its accuracy. In this paper we discuss the employment of correlation clustering algorithms for a robust identification of PWA models with reduced complexity.