| Elman、LS-SVM方法在ET_0预测中的对比分析 |
| |
| 引用本文： | 李帅莹.Elman、LS-SVM方法在ET_0预测中的对比分析[J].农业机械化与电气化,2011(6):88-90. |
| |
| 作者姓名： | 李帅莹 |
| |
| 作者单位： | 辽宁省水利水电勘测设计研究院; |
| |
| 摘 要： | 参考作物腾发量（ET0）是估算作物腾发量的关键参数,其准确预测对提高作物需水预报精度具有十分重要的意义。Elman神经网络是BP网络的改进结构,具有适应时变性的特点;最小二乘支持向量机（LS-SVM）是支持向量机（SVM）的一种优化算法,它基于结构风险最小化准则,可兼顾模型的经验风险和推广能力。将两种方法应用于参考作物腾发量预测中,并以铁岭市为例,对比分析LS-SVM模型与Elman模型的预测值。结果表明：LS-SVM模型学习速度快,具有比Elman模型更高的模拟性能和预测精度,更适合参考作物腾发量的预测。
|
| 关 键 词： | 参考作物腾发量 支持向量机 神经网络 LS-SVM Elman神经网络 |
Predicting Reference Evaportranspiration Based on Elman and LS-SVM |
| |
| Authors: | LI Shuaiying |
| |
| Institution: | LI Shuaiying(Investigation and Design Institute of Water Resources and Hydropower of Liaoning Province,Shenyang 110006,China) |
| |
| Abstract: | Reference evaportranspiration is a key parameter in estimating crop evaportranspiration.Its prediction is very important in estimating crop evapotranspiration and in improving the using efficiency of agricultural water.The Elman neural network is a dynamic neural network based on BP neural network.LS-SVM is an improvement of SVM algorithm.It is based on the minimum structure risk,which can give dual attention to the experience risk of the model and promoted ability.Elman and LS-SVM was used in reference eva... |
| |
| Keywords: | reference evaportranspiration SVM neural network LS-SVM Elman neural network |
| 本文献已被 CNKI 维普 等数据库收录！ |
