基于空间拟合和神经网络的机场噪声预测集成模型  

Airport noise prediction ensemble model based on space fitting and neural network

作　　者：徐涛[1,2] 苏瀚[1] 杨国庆 

机构地区：[1]中国民航大学计算机科学与技术学院,天津300300 [2]中国民航信息技术科研基地,天津300300

出　　处：《中国环境科学》2016年第4期1250-1257,共8页China Environmental Science

基　　金：国家自然科学基金重点项目(61139002);国家"863计划"项目(2012AA063301);国家科技支撑计划项目(2014BAJ04B02);中国民用航空局科技项目(MHRD201006;MHRD201101);中央高校基本科研业务费专项资金(3122013P013)

摘　　要：将集成学习方法引入到机场噪声预测中,提出一种基于空间拟合和神经网络的机场噪声预测集成模型.该模型采用空间拟合算法和BP神经网络算法构建基学习器,然后通过所提出的基于观察学习的异构集成算法将基学习器集成起来,获得集成的机场噪声预测结果.该模型通过集成多个异构机场噪声预测基学习器,能够有效提升预测准确率.实验结果表明,本文所提出的基于观察学习的异构集成算法,较之其他异构集成算法,在解决机场噪声预测问题上准确性更高、容错性更强.This paper proposes an airport noise ensemble prediction model based on space fitting and neural network by introducing ensemble learning method. Space fitting and BP neural network is used respectively to create the base learner and a heterogeneous ensemble algorithm based on observational learning is used to integrate these base learners. The final ensemble model thus can improve prediction accuracy effectively by integrating multiple heterogeneous base prediction learners. The experimental results shows that the proposed heterogeneous ensemble algorithm based on observational learning is better than other heterogeneous ensemble algorithms on accuracy and tolerance for solving the airport noise prediction problem.

关 键 词：机场噪声预测 空间拟合 神经网络 集成学习 观察学习 

分 类 号：X707[环境科学与工程—环境工程] X738.2