摘要
针对传统的高空气象数据挖掘方法中存在的数据挖掘深度问题,提出一种基于BP神经网络技术的高空气象数据挖掘方法。采用BP神经网络技术以及小波分析法对数据挖掘模型进行优化,引进协同多分类器算法进行更加精确的数据挖掘,避免数据产生的干扰。提出的基于BP神经网络的高空气象数据挖掘方法提高了数据挖掘的深度,还对数据的特征提取起到了一定的辅助作用。为了验证该方法的有效性,设计了对比仿真试验,将所提方法与传统方法相比较得出,所提方法有效地解决了数据干扰问题,提高了数据挖掘程度。
In allusion to the data mining depth problem existing in the traditional high-altitude meteorological data mining method, a high-altitude meteorological data mining method based on BP neural network technology is proposed. The BP neural network technology and the wavelet analysis method are adopted to optimize the data mining model. The cooperative multi-classi- fier algorithm is introduced to perform more accurate data mining and avoid data interference. The proposed high-ahitude meteo- rological data mining method based on BP neural network has increased the depth of data mining and played an auxiliary role in data feature extraction. To verify the validity of the method, a simulation test in contrast with the traditional method was de- signed and carried out. The results show that the proposed method can effectively resolve the problem of data interference and improve the degree of data mining.
出处
《现代电子技术》
北大核心
2017年第24期40-42,共3页
Modern Electronics Technique
基金
黑龙江省科技厅自然科学基金项目(F2016032)
关键词
数据挖掘方法
高空气象数据
BP神经网络
小波分析法
模型优化
data mining method
high-altitude meteorological data
BP neural network
wavelet analysis method
model optimization
