基于快速极限学习机和差分进化的机场噪声预测模型

徐涛; 郭威; 吕宗磊

doi:10.11999/JEIT150986

基于快速极限学习机和差分进化的机场噪声预测模型

doi: 10.11999/JEIT150986

2.
(中国民航大学计算机科学与技术学院天津 300300) ②(南京航空航天大学计算机科学与技术学院南京 210016) ③(盐城师范学院信息工程学院盐城 224002)

基金项目:

国家自然科学基金重点项目(61139002)，国家科技支撑计划课题(2014BAJ04B02)，中央高校基本科研业务费专项资金(3122014D032)，中国民航信息技术科研基地开放基金课题(CAAC- ITRB-201401)

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出版历程
- 收稿日期: 2015-09-06
- 修回日期: 2016-01-20
- 刊出日期: 2016-06-19

Prediction Model of Airport?Noise Based on Fast Extreme Learning Machine and Differential Evolution

Funds:

The Key Program of the National Natural Science Foundation of China (61139002), The National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2014BAJ04B02), The Fundamental Research Funds for the Central Universities of Ministry of Education of China (3122014D032), The Open Project Foundation of Information Technology Research Base of Civil Aviation Administration of China (CAAC-ITRB-201401)

摘要

摘要: 该文针对传统机场噪声预测模型存在的建模成本高、实用性差的不足，引入时间序列相空间重构理论，提出一种新的基于快速极限学习机和差分进化算法的机场噪声一体化预测模型。该模型利用相空间重构理论对机场噪声时间序列进行重构，并使用快速极限学习机对重构的相空间矢量进行学习建模，同时采用改进的差分进化算法实现对重构参数和模型参数的同步优化选择，整个建模过程简洁高效，无需人工干预。实验结果表明，该一体化预测模型能较好地跟踪机场噪声的变化趋势，且具有较同类模型更小的预测误差。
- 机场噪声预测 /
- 快速极限学习机 /
- 差分进化 /
- 相空间重构
Abstract: Traditional airport noise prediction models are insufficient for their high modeling cost and poor practicability. In this paper, the time series phase space reconstruction theory is introduced, and a novel integrated airport noise prediction model based on fast extreme learning machine and differential evolution is proposed. In the proposed model, the airport noise time series is reconstructed based on the phase space reconstruction theory, and the fast extreme learning machine is used to model the reconstructed phase space vector. Meanwhile, an improved differential evolution algorithm is adopted to search for the optimal parameter combination of phase space reconstruction parameter and model parameter simultaneously. The whole modeling process of the integrated prediction model is very simple and?efficient without any manual?intervention. Experimental results demonstrate that the proposed model can track the variation tendency of airport noise well and can achieve much more accurate prediction results than its counterparts.
- Airport noise prediction /
- Fast extreme learning machine /
- Differential evolution /
- Phase space reconstruction

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参考文献(22)

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