人民珠江Issue(2)：29-32,4.DOI:10.3969/j.issn.1001-9235.2015.02.009
果蝇优化算法与支持向量机在年径流预测中的应用
Application of Fly Optimization Algorithmand and Support Vector Machine in Annual Runoff Prediction
崔东文 1金波1
作者信息
- 1. 云南省文山州水务局，云南 文山 663000
- 折叠
摘要
Abstract
According to the support vector machine ( SVM) learning parameters are difficult to determine, using Drosophila optimization algorithm ( FOA) search SVM learning parameters———the penalty factor and kernel parameter, put forward FOA -SVM prediction model, and construct based on particle swarm optimization ( PSO) algorithm, a genetic optimization ( GA) algorithm to search the SVM for learning parameters of PSO-SVM model and GA-SVM model as a comparison, in Yunnan Province, Dong Lake Station annual runoff prediction for case study. The results show that: the FOA-SVM model prediction accuracy is better than PSO-SVM and GA-SVM models, have higher prediction precision and generalization ability.关键词
径流预测/果蝇优化算法/支持向量机Key words
Runoff forecasting/Fly optimization algorithmand/Support vector machine分类
天文与地球科学引用本文复制引用
崔东文,金波..果蝇优化算法与支持向量机在年径流预测中的应用[J].人民珠江,2015,(2):29-32,4.
