文章基本信息

标题：Artificial neural networks in forecasting maximum and minimum relative humidity
本地全文：下载
作者：Amanpreet Kaur ; J K Sharma ; Sunil Agrawal 等
期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2011
卷号：11
期号：5
页码：197-199
出版社：International Journal of Computer Science and Network Security
摘要：In this paper, the application of neural networks to study the maximum and minimum relative humidity for Chandigarh city is explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model forecasting system is used and Back Propagation algorithm is used to train the network. The proposed network is trained with actual data of the past 10 years (2000-2010) and tested which comes from meteorological department. The results show that the maximum and minimum relative humidity can be predicted more accurately by using the artificial neural network.
关键词：Artificial neural network; Multi-layer perceptron; Back Propagation