摘要
在现行通用噪声计算模型基础上建立了一种基于模糊支持向量回归的机场噪声预测模型,通过计算样本的局部孤立因子来确定各个样本的模糊隶属度,以融入模糊支持向量回归算法中。最后,在某机场历史飞行数据的基础上,从对所提出模型的预测精度、抗干扰性、泛化能力进行了验证。结果表明,这种基于局部孤立因子的模糊支持向量回归算法能有效地预测机场周围的噪声等级,且该方法比标准支持向量回归具有更高的预测精度和更好的抗噪声能力。
To predict and prevent the noise around the airport becomes an urgent problem.A new airport noise prediction model based on fuzzy support vector regression is established on the existing generic software for noise calculation.To integrate into the fuzzy support vector regression algorithm,the fuzzy membership of each sample is determined by its local outlier factor.Finally,the prediction accuracy,noise immunity,generalization ability of the proposed model are validated on the historic flight data of an airport.Experiments show that the fuzzy support vector regression algorithm based on local outlier factor can effectively predict the noise levels around airports,and is more accurate and better noise immunity than the standard support vector regression.
出处
《南京航空航天大学学报》
EI
CAS
CSCD
北大核心
2013年第5期722-726,共5页
Journal of Nanjing University of Aeronautics & Astronautics
基金
国家自然科学基金重大(61139002)资助项目
中国民航信息技术科研基地开放基金(CAAC-ITRB-201203)资助项目
关键词
机场噪声预测
支持向量机
模糊支持向量回归
模糊隶属度
局部孤立因子
airport noise prediction
support vector machine
fuzzy support vector regression
fuzzy membership
local outlier factor
