Nonlinear term selection and parameter estimation in the identification of nonlinear reduced order state space models

被引：0

作者：

Docter, W ^{[1
]}

Georgakis, C ^{[1
]}

机构：

[1] Lehigh Univ, Chem Proc Modeling & Control Res Ctr, Bethlehem, PA 18015 USA

来源：

DYNAMICS & CONTROL OF PROCESS SYSTEMS 1998, VOLUMES 1 AND 2 | 1999年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a general methodology for the development of Nonlinear Low Order Models (NLLOM) from data collected from detailed nonlinear simulation models. This methodology is divided into two tasks: development of a Average Linear Low Order Model (ALLOM) and augmentation of the ALLOM to form a NLLOM, the latter task being the focus of this paper. The tools examined for the nonlinear augmentation of the ALLOM include stepwise regression and nonlinear optimization. Results will be presented for the application of these techniques towards the development of an NLLOM from a detailed high purity distillation simulation. Copyright (C) 1998 IFAC.

引用

页码：335 / 340

页数：6

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