Application of a general multi-model approach for identification of highly nonlinear processes-a case study

Abstract

An identification method for highly nonlinear processes is proposed based on a multi-model approach and Kolmogorov-Gabor polynomials. Owing to the large number of possible terms in this general model structure, the significant terms are selected by several statistical test procedures leading automatically to a minimal-order model realization. The performance of this method is evaluated in an in-depth case study using a simulated p^H neutralization process. The effects of important variables such as range of operating conditions, signal-/noise-ratio, and data length are discussed.

Additional information

Notes on contributors

M. POTTMANN

Currently with the Department of Machine and Process Automation, Technical University Vienna, 1040 Wien, Austria.

H. UNBEHAUEN

On sabbatical leave from the Department of Electrical Engineering, Ruhr University, 4630 Bochum, Germany.