輻狀基底函數網路於水文系統之研究(1/3)
Date Issued
2005
Date
2005
Author(s)
DOI
932211E002026
Abstract
This study is the first year’s work of a three-year project. In the project, the radial basis function
network (RBFN) is used to construct a rainfall-runoff model, and the fully supervised learning
algorithm is presented for the parametric estimation of the network. The number of hidden layer
neurons can be constructed automatically and the training error decreases with increasing number of
neurons. The fully supervised learning algorithm has advantages over the hybrid-learning
algorithm that has the trouble of setting up the number of hidden layer neurons. Furthermore, early
stopping technique is used to cease training, which can avoid over-fitting during the process of
network construction. Finally, the proposed methodology is applied to the Fei-tsui Reservoir
watershed to forecast the one-hour ahead inflow. The result shows that the RBFN can be applied
to build the relation of rainfall and runoff successfully.
Subjects
artificial neural network
radial basis function
flow forecasting
Publisher
臺北市：國立臺灣大學土木工程學系暨研究所
Type
report
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