摘要
以基本遗传算法为基础,优化人工神经网络与最近邻耦合模型的基本参数,得到无参数的自适应NNBR-ANN耦合模型。应用此模型对黄河青铜峡年平均流量进行预测,并与单独的人工神经网络模型和最近邻抽样回归模型预测结果进行比较分析。结果表明:此方法将模型的基本参数进行优化处理,打破传统的定参方法,用于径流预测更加方便适用,且预测精度更高。
Based on genetic algorithm on optimizing the parameter of artificial neural network and nearest neighbor beotstrapping regressive model, the automatic coupling model of NNBR-ANN is obtained. The model is applied to forecast the annual nmoff of QingTong Xia of Yellow River and compare with Artificial neural network and nearest neighbor bootstrapping regressive model. It shows that the model could optimize the basic parameter and break traditional method of deciding parameter. The coupling model is smart applicable and has better foresting precision.
出处
《四川大学学报（工程科学版）》
EI
CAS
CSCD
北大核心
2005年第5期5-8,共4页
Journal of Sichuan University (Engineering Science Edition)
基金
国家自然科学重点基金资助项目(40271024
50279023)
关键词
中长期预测
耦合模型
人工神经网络
最近邻抽样回归模型
遗传算法
mid-long nmoff prediction
coupling model
Artificial Neural Network
nearest neighbor bootstrapping regressive model
genetic algorithm
