期刊
BUILDING AND ENVIRONMENT
卷 43, 期 6, 页码 1121-1126出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.buildenv.2007.03.003
关键词
indoor air quality; indoor environmental quality; office buildings; building-related symptoms; artificial neural networks
Artificial neural networks (ANN) were constructed to predict prevalence of building-related symptoms (BRS) of office building occupants. Six indoor air pollutants and four indoor comfort variables were used as input variables to the networks. A symptom metric was used as the measure of BRS prevalence, and employed as the output variable. Pollutant concentration, comfort variable, and occupant symptom data were obtained from the Building Assessment and Survey Evaluation study conducted by the US Environmental Protection Agency, in which all were measured concurrently. Feed-forward networks that employ back-propagation algorithm with momentum term and variable learning rate were used in ANN modeling. Root mean square error and R 2 value of the simple linear regression between observed and predicted output were used as performance measures. Among the constructed networks, the best prediction performance was observed in a one-hidden-layered network with an R-2 value of 0.56 for the test set. All constructed networks except one showed a better performance than the multiple linear regression analysis. (C) 2007 Elsevier Ltd. All rights reserved.
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