A new data-based methodology for nonlinear process modeling

Abstract

A new data-based method for nonlinear process modeling is developed in this paper. In the proposed method, both distance measure and angle measure are used to evaluate the similarity between data, which is not exploited in the previous work. In addition, parametric stability constraints are incorporated into the proposed method to address the stability of local models. Furthermore, a new procedure of selecting the relevant data set is proposed. Literature examples are presented to illustrate the modeling capability of the proposed method. The adaptive capability of the proposed method is also evaluated. © 2004 Elsevier Ltd. All rights reserved.