河南科技大学学报（自然科学版）2024，Vol.45Issue(2)：96-104,9.DOI:10.15926/j.cnki.issn1672-6871.2024.02.011
基于SDAE的终端区气象场景模式识别方法
Terminal Area Meteorological Scenario Pattern Recognition based on SDAE
摘要
Abstract
To improve the accuracy of terminal area meteorological scene pattern recognition,this study adopts a clustering model based on Stacked Denoising Autoencoder.Noise is added to the input layer,and a three-layer autoencoder is constructed for greedy layer-wise training.The reduced-dimensional features are used as inputs for clustering to achieve meteorological scene pattern recognition.The method is validated using one year of meteorological data from Tianjin Binhai International Airport.Traditional similarity distance measures such as Euclidean distance,Hamming distance,and Manhattan distance are used with both K-medoids and FCM clustering methods.The results show that the similarity measure based on SDAE performs the best in both K-medoids and FCM clustering,with a difference rate of 22.4%,12%,17.7%,and 24.8%,10.7%,11.8%compared to other similarity measures,respectively.It also has the shortest computation time,demonstrating that the SDAE-based measure and clustering achieve the best performance.Ultimately,eight meteorological scenes are identified with clear and distinct classifications.关键词
气象特征/堆叠降噪自编码/K-medoids/FCMKey words
meteorological characteristics/stacked denoising autoencoder/K-medoids/FCM分类
航空航天引用本文复制引用
杨新湦,罗秋晴,张召悦..基于SDAE的终端区气象场景模式识别方法[J].河南科技大学学报（自然科学版）,2024,45(2):96-104,9.基金项目
国家自然科学基金青年科学基金项目(71801215old) （71801215old）
国家重点研发计划项目(KJZ25420200012) （KJZ25420200012）
