LAUSR

Abstract In this paper an automated model generation framework is used to identify three nonlinear dynamic benchmark processes. The nonlinearity is approximated using tree-based local model networks (LMN) with external dynamics, which are represented by three different approaches: NARX, NFIR and NOBF. The automated method assumes no prior knowledge about the process, and aims to be a ready-to-use tool for system identification. Results are given for the different approaches and benchmark processes. The importance of the choice of training data for a good generalizing model performance is discussed.