水力发电学报2011，Vol.30Issue(3)：50-56,7.
自我调节蚁群-RBF神经网络模型在短期径流预测中的应用
Short-term runoff prediction based on adaptive regulation ant colony system and radial basis function neural network hybrid algorithm
摘要
Abstract
Short-term runoff prediction is a major responsibility of water conservancy departments.To improve the accuracy of reservoir short-term runoff forecast, an adaptive regulation ant colony system (ARACS)algorithm is proposed.A forecast model was developed by using this new algorithm in combination with a radial basis function (RBF) neural network that was trained by using ARACS and an ARACS-RBF hybrid algorithm was obtained.This model can automatically determine the parameters of the neural network from the data sample.Predictions of the short-term runoff of practical reservoirs were made using the model with a comprehensive consideration of impacting factors such as meteorology, weather, rainfall and seasonal change.The result shows a faster convergence and a better forecast accuracy of the hybrid method than those of the traditional ant colony system algorithm-RBF neural network or RBF neural network, and also a significant improvement on the generalization capacity of RBF neural network.An average percentage error of no more than 3％ was achieved.Thus, the hybrid algorithm enhances the efficiency of short-term load forecast of the reservoir and river.关键词
水文学/ARACS-RBF神经网络模型/自适应调节蚁群算法/短期径流预测/RBF神经网络Key words
hydrology/ ARACS-RBF hybrid algorithm model/ adaptive regulation ant colony system algorithm/ short-term runoff forecast/ the radial basis function neural network分类
信息技术与安全科学引用本文复制引用
白继中,师彪,冯民权,周利坤,李小龙..自我调节蚁群-RBF神经网络模型在短期径流预测中的应用[J].水力发电学报,2011,30(3):50-56,7.基金项目
国家火炬计划基金(07C26213711606) （07C26213711606）
山西省水利厅科技计划基金(2009WK110) （2009WK110）
