A novel approach to model determination using the minimum model error estimation

Jason R. Kolodziej; D. Joseph Mook

A novel approach to model determination using the minimum model error estimation

Jason R. Kolodziej
, D. Joseph Mook

Department of Mechanical and Aerospace Engineering

General Motors

Research output: Contribution to journal › Conference article › peer-review

3 Scopus citations

Abstract

The purpose of this paper is to present an algorithm for the combination of a proven nonlinear system identification technique, the Minimum Model Error estimation algorithm (MME) with an Analysis of Variance (ANOVA) correlation routine where a forward stepwise procedure is implemented. The Analysis of Variance approach to model identification is well documented primarily in social science literature but has been sparsely written about for engineering applications. This paper will show a significant improvement in nonlinear model identification when used in conjunction with the MME algorithm.

Original language	English
Article number	FrC15.1
Pages (from-to)	5046-5051
Number of pages	6
Journal	Proceedings of the American Control Conference
Volume	7
State	Published - 2005
Event	2005 American Control Conference, ACC - Portland, OR, United States Duration: Jun 8 2005 → Jun 10 2005

Cite this

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title = "A novel approach to model determination using the minimum model error estimation",

abstract = "The purpose of this paper is to present an algorithm for the combination of a proven nonlinear system identification technique, the Minimum Model Error estimation algorithm (MME) with an Analysis of Variance (ANOVA) correlation routine where a forward stepwise procedure is implemented. The Analysis of Variance approach to model identification is well documented primarily in social science literature but has been sparsely written about for engineering applications. This paper will show a significant improvement in nonlinear model identification when used in conjunction with the MME algorithm.",

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AU - Joseph Mook, D.

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AB - The purpose of this paper is to present an algorithm for the combination of a proven nonlinear system identification technique, the Minimum Model Error estimation algorithm (MME) with an Analysis of Variance (ANOVA) correlation routine where a forward stepwise procedure is implemented. The Analysis of Variance approach to model identification is well documented primarily in social science literature but has been sparsely written about for engineering applications. This paper will show a significant improvement in nonlinear model identification when used in conjunction with the MME algorithm.

UR - https://www.scopus.com/pages/publications/23944446529

M3 - Conference article

AN - SCOPUS:23944446529

SN - 0743-1619

VL - 7

SP - 5046

EP - 5051

JO - Proceedings of the American Control Conference

JF - Proceedings of the American Control Conference

M1 - FrC15.1

T2 - 2005 American Control Conference, ACC

Y2 - 8 June 2005 through 10 June 2005

ER -

A novel approach to model determination using the minimum model error estimation

Abstract

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