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
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;
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
DOI :
10.1109/WCICA.2008.4594413
