摘要
通过分析影响单个飞行事件噪声的各种因素,构建了BP神经网络回归预测模型,并通过自适应遗传算法优选出参与集成的个体神经网络,提出了预测单个飞行事件噪声的神经网络集成预测模型.为了有效保证差异性,设置不同隐藏神经元个数和Bagging算法来构建和训练单个网络.实验结果表明,单个飞行事件噪声的神经网络集成预测模型相对单个BP神经网络模型泛化能力更强,稳定性能更好.本文方法在测试集上误差在3dB以内的平均比率为96.9%,比单个网络高6.8%.
Through analyzing the influence factors of the noise event for single flight, the regression prediction model based on BP neural network was established. Then, the ensemble prediction model based on neural network for single noise event was constructed by selecting neural networks with the aid of adaptive genetic algorithm. Simultaneously, in order to maintain the diversity of neural networks, different number of hidden nodes and Bagging algorithm were used. Experimental results show that the proposed ensemble prediction model based on neural network was better than the model of single BP neural network in terms of generalization ability and higher stability. The average accuracy rate of the proposed model was 96.9% on the test set within ＋3dB error and was 6.8% higher than that of the single network model.
出处
《中国环境科学》
EI
CAS
CSCD
北大核心
2014年第2期539-544,共6页
China Environmental Science
基金
国家自然科学基金项目(61139002)
国家"863"项目(2012AA063301)
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)
关键词
单个飞行事件噪声
预测模型
BP神经网络
神经网络集成
遗传算法
noise event for single flight
prediction model
BP neural network： neural network ensemble
geneticalgorithm
