基于GM-LSSVR机场噪声时间序列预测模型  

Prediction Model for Airport-noise Time Series Based on GM-LSSVR

作　　者：温冬琴[1] 王建东[1] 张霞[1] 

机构地区：[1]南京航空航天大学计算机科学与技术学院,南京210016

出　　处：《计算机科学》2013年第9期198-200,220,共4页Computer Science

基　　金：面向机场感知的噪声监测及其环境影响评估(61139002)资助

摘　　要：随着我国民航事业的不断发展,机场噪声问题日益严重。针对机场噪声时间序列预测问题,在分析了噪声数据特点的基础上,提出了一种基于GM-LSSVR的噪声序列预测模型,即将机场噪声时间序列进行分解,并分别建立趋势项的GM(1,1)模型和剩余项的LSSVR模型,用于机场噪声预测值的计算。在某机场实测数据中的应用表明,该方法的准确度明显优于其他预测方法。Along with the development of our country civil aviation, the airport noise is more and more serious. Aimed at the airport-noise time sequence prediction problem, this paper presented the prediction model based on GM-LSSVR, which is based on the ananlyses of noise＇s characteristic. Namely, the model decomposes the noise time sequence into tendency and the residual, then establishes the GM（1,1）model of trend and the LSSVR model of residual, which are used for airport-noise prediction model. The application in the measured data of an airport shows that the accuracy of this model is better than other prediction models.

关 键 词：时间序列分解 机场噪声预测模型 GM（1 1） 最小二乘支持向量机 Spearman 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]