• 2018-05-26 星期六
•  doi:  10.3878/j.issn.1006-9585.2017.17097 利用慢特征分析法提取二维非平稳系统中的外强迫特征 Extracting the driving force signal from two-dimensional non-stationary system based on slow feature analysis 摘要点击 500  全文点击 31  投稿时间：2017-07-05  修订日期：2017-10-13 查看HTML全文   下载PDF   查看/发表评论  下载PDF阅读器 基金:  本文得到国家自然科学基金(批准号: 41575058) 项目的资助 中文关键词:  慢特征分析法, 二维非平稳系统, 外强迫信号 英文关键词: 引用:范开宇,王革丽,李 超,潘昕浓.2018.利用慢特征分析法提取二维非平稳系统中的外强迫特征[J].气候与环境研究 Citation:Fan Kai-Yu,Wang Ge-Li,Li Chao,Pan Xin-Nong.2018.Extracting the driving force signal from two-dimensional non-stationary system based on slow feature analysis[J].Climatic and Environmental Research(in Chinese) 中文摘要: 慢特征分析法(SFA) 是一个从快变的信号中提取慢变特征的有效方法,它的提出丰富了人们对非平稳系统外强迫特征的重建手段。本文以Henon 映射为基础,构造二维非平稳系统模型,尝试SFA 方法在二维复杂非平稳系统中重建外强迫特征的能力。试验表明,SFA 方法能够较好的从单时变参数Henon 映射中提取出外强迫信号；通过结合小波变换技术,可以还原双时变参数Henon 映射中的外强迫信号。另外,本文利用SFA 方法重建了北京市气温的外强迫信号,分析其外强迫信号的尺度特征及其可能的物理机制。这些工作将为气候系统驱动力的研究提供新的思路。 Abstract: Slow feature analysis (SFA) is an effective method for extracting slow-changing feature from fast-changing signal. Its proposal enriches the means of reconstruction of non-stationary system’s driving force signal. In this paper, we constructed two-dimensional non-stationary system model based on Henon chaotic mapping, and tried to test the ability of reconstructing driving force signal from two-dimensional and complex non-stationary system by SFA method. The experimental results showed that the SFA can successfully extract the driving force signal from the non-stationary time series with one time-varying parameter. We also extracted the driving force signals from the non-stationary time series with two time-varying parameters by SFA and wavelet transform technology. In addition, by using SFA method, we reconstructed the driving force of Beijing air temperature. Wavelet transformation technique was then used to analyze the scale structure of the derived driving force. These efforts will provide new ideas for the study of climate system’s driving force.
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