Implementation of nonlinear inferential sensors

Author

Neelakantan, Ramesh

Author_Institution

Aspen Technol. Inc., Pittsburgh, PA, USA

Volume

5

fYear

1999

fDate

1999

Firstpage

3123

Abstract

In industrial applications of inferential sensing, the most popular technology deployed is neural network technology. However, the industry needs more than one modeling option for building inferential sensors. Statistical tools such as partial least squares (PLS) must be provided to the user as options. Use of fuzzy functions and a hybrid of PLS and neural network are also becoming popular because of the robustness in predictions. First principle models are also used where data is not reliable. Successful implementation of an inferential sensor project requires a range of choices in modeling tools and good project engineering. This paper focuses on empirical model based inferential sensor and the implementation steps to accomplish a successful inferential sensing project

Keywords

data acquisition; fuzzy set theory; least squares approximations; neural nets; parameter estimation; principal component analysis; fuzzy functions; inferential sensors; neural network; partial least squares; principal component analysis; Artificial neural networks; Cost benefit analysis; Extrapolation; Fuzzy neural networks; Input variables; Laboratories; Least squares methods; Mathematical model; Neural networks; Predictive models;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 1999. Proceedings of the 1999

Conference_Location

San Diego, CA

ISSN

0743-1619

Print_ISBN

0-7803-4990-3

Type

conf

DOI

10.1109/ACC.1999.782338

Filename

782338