Sensor Fault Identification and Reconstruction Using Principal Component Analysis-Reference-Cited by-同舟云学术

Sensor Fault Identification and Reconstruction Using Principal Component Analysis

Published:1996-06 Issue:1 Volume:29 Page:6578-6583
ISSN:1474-6670
Container-title:IFAC Proceedings Volumes
language:en
Short-container-title:IFAC Proceedings Volumes

Author:

Dunia Ricardo,Joe Qin S.,Edgar Thomas F.,McAvoy Thomas J.

Publisher

Elsevier BV

Subject

General Medicine

Reference9 articles.

1. Statistical Factor Analysis and Related Methods;Basilevski,1994

2. Applications of Autoassociative Neural Networks for Signal Validation in Accident Management;Fantoni;Proceedings of the IAEA Specialist Meeting on Advanced Information Methods and Artificial Intelligence in Nuclear Power Plant Control Rooms,1994

3. Partial Least Squares Regression: A Tutorial;Geladi;Analytica Chimica Acta,1986

4. A User's Guide to Principal Components;Jackson,1991

5. Autoassociative Neural Networks;Kramer;Computers Chem. Engng,1992

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