NDLI: Reduced-order functional link neural network for HVAC thermal system identification and modeling

Content Provider	IEEE Xplore Digital Library
Author	Mo-Yuen Chow Teeter, J.
Copyright Year	1997
Description	Author affiliation: Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC, USA (Mo-Yuen Chow)
Abstract	The use of computers for direct digital control highlights the trend toward more effective and efficient HVAC control methodologies. Researchers in the HVAC field have stressed the importance of self-learning in building control systems and the integration of optimal control and other advanced techniques into the formulation of such systems. This paper describes a functional link neural network approach to perform the HVAC thermal system identification and modeling. Artificial neural networks are used to emulate the plant dynamics in order to estimate future plant outputs and obtain plant input/output sensitivity information for online neural control adaptation. Methodologies to appropriately reduce the inputs, thus the complexity, of the functional link network in order to speed up the training are presented. This paper also analyzes and compares the performance and complexity between the functional link network and conventional network approaches for the HVAC thermal system identification and modeling.
Starting Page	5
Ending Page	9
File Size	447306
Page Count	5
File Format	PDF
ISBN	0780341228
DOI	10.1109/ICNN.1997.611625
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-06-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks System identification Temperature Artificial neural networks Optimal control Space heating Digital control Control systems Performance analysis Intelligent control
Content Type	Text
Resource Type	Article

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