Smart technique for identifying hybrid systems

The paper describes a system identification method for a nonlinear system based on a multi-point linear approximation. We show that under mild assumptions, the task can be transformed into a series of one-dimensional approximations, for which we propose an efficient solution method based on solving simple nonlinear programs (NLPs). The approach provides identification of nonlinear systems in a polynomial model structure (ARX, OE, BJ) from input-output data. The approximation is based on a neural network modelling procedure. The proposed modelling procedure is characterized by fast training, adjustable accuracy and reduced complexity of the final model. The modelling technique is widely applicable in automotive, power electronics, computer graphics, etc.