NDLI: Self-learning neurocontroller for maintaining indoor relative humidity

Content Provider	IEEE Xplore Digital Library
Author	Sigumonrong, A.P. Bong, T.Y. Fok, S.C. Wong, Y.W.
Copyright Year	2001
Description	Author affiliation: Div. of Thermal & Fluids Eng., Nanyang Technol. Inst., Singapore (Sigumonrong, A.P.)
Abstract	An air-conditioning system is designed to meet maximum space cooling load. Thus the system's controller needs parameter adjustment periodically due to changes in the environment and operating conditions. For a constant-air-volume system at system part-load operation the indoor relative humidity may exceed the limit recommended for comfort and health. This paper describes the application of neural networks to develop an intelligent air handling. The purpose is twofold: 1) the controller self-learning capability will substitute conventional parameter adjustment; 2) in addition to controlling the indoor temperature, the controller will also limit the indoor relative humidity. With the designed cost function, the proposed controller is a promising tool to limit the rise in indoor relative humidity in this particular constant-air-volume system.
Sponsorship	Int. Neural Network Soc.
Starting Page	1297
Ending Page	1301
File Size	310657
Page Count	5
File Format	PDF
ISBN	0780370449
ISSN	10987576
DOI	10.1109/IJCNN.2001.939548
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurocontrollers Humidity control Temperature control Control systems Artificial neural networks Neurons Neural networks Intelligent networks Cost function Automatic control
Content Type	Text
Resource Type	Article

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