An easily trained neural model of a distributed parameter system

Maciej Ławryńczuk

Abstract

This paper is concerned with black-box modelling of a distributed parameter thermal system (a long duct) by means of neural networks. A new model structure is discussed which consists of a set of local neural sub-models and a neural interpolator. The local sub-models calculate temperatures for a number of predefined locations of sensors. They are trained independently, using limited data sets. Next, the neural interpolator, using the local temperatures modelled by the sub-modes, calculates the value of the temperature for any sensor location. The interpolator is also trained independently. This paper also discusses the method of choosing which local sub-models should be actually used. It is shown that for the initial structure with 10 sub-models as many as 6 or 7 of them may be removed without significant deterioration of overall model accuracy.
Author Maciej Ławryńczuk IAiIS
Maciej Ławryńczuk,,
- The Institute of Control and Computation Engineering
Pages674-679
Publication size in sheets0.5
Book Proceedings of 21st IEEE Conference on Method and Models in Automation and Robotics, 2016, IEEE Institute of electrical and Electronics Engineers, ISBN 978-1-5090-1715-7, 1285 p., DOI:10.1109/MMAR.2016.7575223
Keywords in EnglishTemperature sensors, Training, Ducts, Data models, Neural networks, Temperature measurement
DOIDOI:10.1109/MMAR.2016.7575217
URL http://ieeexplore.ieee.org/document/7575217/
projectDevelopment of methodology of control, decision support and production management. Project leader: Zieliński Cezary, , Phone: 5102, start date 19-05-2015, end date 31-12-2016, 504/02233/1031, Completed
WEiTI Działalność statutowa
Languageen angielski
File
LawrynczukMMAR16.pdf 1.4 MB
Score (nominal)15
ScoreMinisterial score = 15.0, 27-03-2017, BookChapterMatConf
Ministerial score (2013-2016) = 15.0, 27-03-2017, BookChapterMatConf
Citation count*0 (2018-06-16)
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