摘要
针对水质预测过程中样本数据少的特点,引入了改进证据理论和灰色神经网络相结合的组合预测方法。首先利用灰色神经网络作为单一模型对水质进行初步预测,再用神经网络对预测结果进行分析建模,得到每个单一预测模型的可信度,最后采用改进证据理论进行融合决策,以获得各单一预测模型的权重,从而实现了水质的组合预测。实例分析结果表明,该方法拟合误差小、预测精度高。
Aiming to the characteristics of few sample data in water quality prediction process,this paper introduced a combination prediction meth-od based on improved evidence theory combining with gray neural network. Firstly,the gray neural network was used as single model to preliminary predict the water quality;then the neural network was used to analyze the predictive results and establish models to get the credibility of every single prediction model;finally,the evidence theory was employed to fuse them and to obtain the single prediction mode1 weight. Hence,the water quali-ty prediction was realized. The results show that the method has small fitting error and high prediction precision.
出处
《人民黄河》
CAS
北大核心
2014年第3期46-48,共3页
Yellow River
基金
云南省教育厅科学研究基金资助项目(2013Y065)
关键词
改进证据理论
灰色神经网络
水质
预测
improved evidence theory
gray neural network
water quality
prediction
