DocumentCode
2502797
Title
Neural network water bloom short time forecast based on evidence theory
Author
Wang, Xiaoyi ; Liu, Zaiwen ; Cui, Lifeng ; Lian, Xiaofeng ; Wu, Qiaomei ; Lv, Siying
Author_Institution
Inst. of Inf. Eng., Beijing Technol. & Bus. Univ., Beijing
fYear
2008
fDate
25-27 June 2008
Firstpage
8981
Lastpage
8984
Abstract
Analyzing the characters of water-bloom eruption, one effective model on weightings attribute of forecasting water-bloom based on D-S evidence theory has been proposed. After pre-treating forecast index data, sets up water -bloom short-time forecast model based on neural network, which improves forecast precision of water-bloom, through simulation and testing, the result shows its affectivity and superiority.
Keywords
forecasting theory; inference mechanisms; neural nets; water pollution control; D-S evidence theory; Dempster-Shafer evidence theory; neural network; neural network water bloom short time forecast; short time forecast; water bloom; Automation; Chemical analysis; Chemical engineering; Chemical technology; Electronic mail; Intelligent control; Neural networks; Predictive models; Technology forecasting; Testing; D-S evidence theory; neural network; short-time forecast; water-bloom;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
Conference_Location
Chongqing
Print_ISBN
978-1-4244-2113-8
Electronic_ISBN
978-1-4244-2114-5
Type
conf
DOI
10.1109/WCICA.2008.4594413
Filename
4594413
Link To Document