@inproceedings{65c0eedb6782454985b23dbe8db0873e,
title = "Study on PMV index forecasting method based on fuzzy c-means clustering",
abstract = "In order to improve the forecasting accuracy of indoor thermal comfort, the basic principle of fuzzy c-means clustering algorithm (FCM) and support vector machines (SVM) is analyzed. A kind of SVM forecasting method based on FCM data preprocess is proposed in this paper. The large data sets can be divided into multiple mixed groups and each group is represented by a single regression model using the proposed method. The support vector machines based on fuzzy c-means clustering algorithm (FCM+SVM) and the BP neural network based on fuzzy c-means clustering algorithm (FCM+BPNN) are respectively applied to forecast PMV index. The experimental results demonstrate that the FCM+SVM method has better forecasting accuracy compared with FCM+BPNN method.",
keywords = "BP, FCM algorithm, PMV index, Support vector machines",
author = "Zhang, {Chun Cheng} and Chen, {Xiang Guang} and Xu, {Yuan Qing}",
year = "2012",
doi = "10.4028/www.scientific.net/AMR.383-390.925",
language = "English",
isbn = "9783037852958",
series = "Advanced Materials Research",
pages = "925--930",
booktitle = "Manufacturing Science and Technology",
note = "2011 International Conference on Manufacturing Science and Technology, ICMST 2011 ; Conference date: 16-09-2011 Through 18-09-2011",
}