@inproceedings{d8a9aba509514f648c6fa927a9eff7f7,
title = "Investigation and comparison between GM(1,1) and BPANN forecast models in Shanghai low-rent housing families",
abstract = "Based on the data of household income of Shanghai low-rent housing families, a GM(1,1) forecast model and a Back-Propagation Artificial Neural Network (BPANN) forecast model are established respectively to predict the average household income of low-rent housing families. The comparison between the GM(1,1) and the BPANN model showed that the BPANN model is better than the GM(1,1) model at the aspects of prediction accuracy and data adaptability. The BPANN model could be applied successfully to predict the average household income of Shanghai low-rent housing families in a short-term and it will provide scientific and effective basis for formulate policy on low-rent housing.",
keywords = "BPANN, Comparison, GM(1,1), Low-rent housing families, Prediction model",
author = "Zhuo Li and Jianhua Xu and Qing Wei",
year = "2010",
doi = "10.1109/ICIECS.2010.5678188",
language = "英语",
isbn = "9781424479412",
series = "2nd International Conference on Information Engineering and Computer Science - Proceedings, ICIECS 2010",
booktitle = "2nd International Conference on Information Engineering and Computer Science - Proceedings, ICIECS 2010",
note = "2nd International Conference on Information Engineering and Computer Science, ICIECS 2010 ; Conference date: 25-12-2010 Through 26-12-2010",
}