Cart (Loading....) | Create Account
Close category search window
 

Prediction of Indoor Air Quality Using Artificial Neural Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Hui Xie ; Sch. of Civil & Environ. Eng., Univ. of Sci. & Technol. Beijing, Beijing, China ; Fei Ma ; Qingyuan Bai

This paper described an application of artificial neural networks (ANNs) to predict the indoor air quality (IAQ). Six indoor air pollutants and three indoor comfort variables were used as input variables to the networks. An occupant symptom metric (PIAQ) was used as the measure of indoor air quality, and employed as the output variable.Pollutant concentration, comfort variable, and PIAQ data were obtained from previous studies. Feed-forward networks that employed back-propagation algorithm with momentum term and variable learning rate were used in ANN modeling.Among constructed networks, the best prediction performance was observed in a two-hidden-layered network with the high correlation coefficient and low root mean square error for the test set. Meanwhile, the constructed networks had a better performance than the multiple linear regression analysis. The results showed that the ANN approach can be applied successfully in predicting indoor air quality.

Published in:

Natural Computation, 2009. ICNC '09. Fifth International Conference on  (Volume:2 )

Date of Conference:

14-16 Aug. 2009

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.