NDLI: An Improved Fuzzy RBF Based on Cluster and Its Application in HVAC $System^{*}$

Content Provider	IEEE Xplore Digital Library
Author	Shujiang Li Xiaoqing Zhang Jinxue Xu Wenjian Cai
Copyright Year	2006
Description	Author affiliation: Sch. of Inf. & Sci. Eng., Shenyang Univ. of Technol. (Shujiang Li; Xiaoqing Zhang; Jinxue Xu)
Abstract	To solve the existing problems in modeling the HVAC (heating ventilating and air-conditioning) systems the paper proposed an improved algorithm based on fuzzy RBFNN (RBF neural networks). First, the sampling data of the HVAC system are processed using the improved fuzzy clustering method so as to redescribe the fuzzy space, thus obtaining the number of hidden layers and its parameters. Then the least square method is used to find the weights between the hidden layer and the output layer. At last, the parameters are revised by the steepest gradient descent method. Experiments demonstrated the proposed algorithm could accurately identify the model of HVAC systems even in the face of strong disturbance and system coupling. Moreover, the model outputs could track the actual outputs
Starting Page	6455
Ending Page	6459
File Size	178270
Page Count	5
File Format	PDF
ISBN	1424403324
DOI	10.1109/WCICA.2006.1714328
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy systems Educational technology Paper technology Neural networks Mathematical model Clustering algorithms Energy consumption Cooling Ducts Temperature control RBF neural network Cluster HVAC
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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