Title :
Water-Bloom Medium-Term Prediction Based on Gray-BP Neural Network Method
Author :
Zhu, Shiping ; Liu, Zaiwen ; Wang, Xiaoyi ; Dai, Jun
Author_Institution :
Sch. of Comput. & Inf. Eng., Beijing Technol. & Bus. Univ., Beijing, China
Abstract :
On the basis of studying the mechanism of water bloom, one kind of gray-BP artificial neural network forecasting method is proposed in the paper. The gray theory was used to obtain preliminary forecast of the occurrence trend of water bloom, combined with neural network to implement error compensation for the forecast result. Compared with BP, this method can predict chlorophyll change trend more accurately, and significantly improve the prediction accuracy with the prolongation of prediction period. It provides an effective new method for water bloom medium-term prediction.
Keywords :
backpropagation; error compensation; forecasting theory; neural nets; water; chlorophyll change trend; gray BP neural network method; gray theory; implement error compensation; neural network forecasting method; occurrence trend water bloom; prolongation prediction period; water bloom medium term prediction; Accuracy; Artificial neural networks; Biological system modeling; Computer networks; Error compensation; Lakes; Neural networks; Paper technology; Predictive models; Radial basis function networks; error compensation; gray-BP neural network; water bloom medium-term prediction;
Conference_Titel :
Dependable, Autonomic and Secure Computing, 2009. DASC '09. Eighth IEEE International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-0-7695-3929-4
Electronic_ISBN :
978-1-4244-5421-1
DOI :
10.1109/DASC.2009.14
