An easily trained neural model of a distributed parameter system

Maciej Ławryńczuk


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 (FEIT / AK)
Maciej Ławryńczuk,,
- The Institute of Control and Computation Engineering
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
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
LawrynczukMMAR16.pdf 1.4 MB
Score (nominal)15
Score sourceconferenceIndex
ScoreMinisterial score = 15.0, 02-02-2020, BookChapterMatConfByConferenceseries
Ministerial score (2013-2016) = 15.0, 02-02-2020, BookChapterMatConfByConferenceseries
Publication indicators Scopus Citations = 0; WoS Citations = 0
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