An easily trained neural model of a distributed parameter system
AbstractThis 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.
|Publication size in sheets||0.5|
|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 English||Temperature sensors, Training, Ducts, Data models, Neural networks, Temperature measurement|
|project||Development 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
|Score|| = 15.0, 27-03-2017, BookChapterMatConf|
= 15.0, 27-03-2017, BookChapterMatConf
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