An unrestricted algorithm for accurate prediction of multiple-input multiple-output (MIMO) Wiener processes

Supplemental Files
Date
2004-01-01
Authors
Chin, Swee-Teng
Rollins, Derrick K
Bhandari, Nidhi
Rollins, Derrick
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Research Projects
Organizational Units
Journal Issue
Series
Department
Chemical and Biological Engineering
Abstract

Previous research [N. Bhandari and D. K. Rollins, Ind. Eng. Chem. Res., 2003, 42, 5583] introduced a methodology for obtaining accurate continuous-time multiple-input, multiple-output (MIMO) models for Wiener processes with nonlinear static and dynamic behavior. This methodology consists of a model-building procedure for estimation of model forms in the Wiener structure and a choice of two algorithms for exact predictions of true Wiener systems. One algorithm uses only the most-recent input changes but is restricted to approximately steady-state conditions between input changes. The other algorithm has no restricted conditions but is dependent on all past input changes and, thus, requires a fading memory treatment. This article extends the former algorithm by proposing a new continuous-time algorithm that is not restricted by steady-state conditions between input changes. In addition, the proposed algorithm is dependent only on the most-recent input changes. Evaluation of the proposed algorithm is conducted using a simulated continuously stirred tank reactor (CSTR) that closely follows a Wiener process; the results of this study are compared with the other two previously mentioned algorithms. Results are given for two basic cases: (i) no noise and (ii) independently, identically, and normally distributed noise.

Comments

Reprinted (adapted) with permission from Industrial and Engineering Chemistry Research 43 (2004): 7065, doi: 10.1021/ie0308184. Copyright 2004 American Chemical Society.

Description
Keywords
Citation
DOI
Collections