水电站来水量预测
Water volume Prediction In Hydropower
王海军 1刘文1
作者信息
- 1. 中电投云南国际电力投资有限公司 云南昆明 650228
- 折叠
摘要
本文针对神经网络存在结构较难确定，训练易陷入局部最优以及容易过学习等问题，提出将改进最小二乘支持向量机用于预测水电站来水量。实例分析表明，与基于BP网络的时用水量模型相比，基于改进最小二乘支持向量机的水电站来水量动态变化模型具有更强的预测能力。
Abstract
This paper, as traditional neural network suffers from the problems like the existence of many local minima and the choice of the number of hidden units, and over fiting ameliorate least squares support vector machine is proposed to predict the Hydropower Water volume. Case study shows that ameliorate least squares support vector machine based Hydropower Water volume forecast model has better generalization ability than BP neural network-based forecast model.
关键词
支持向量机/来水量/预测Key words
support vector machine/Water volume/predict引用本文复制引用
出版年
2014
NETL
NSTL
维普
万方数据