Slow feature analysis (SFA) is an effective method for extracting slow-changing features from fast-changing signals. Its proposal enriches the means of reconstruction of non-stationary system's driving force signals. Two-dimensional non-stationary system model be constructed based on Henon chaotic mapping. The authors try to test the ability of reconstructing driving force signals from two-dimensional and complex non-stationary system by SFA method. The experimental results show that the SFA can successfully extract the driving force signals from the non-stationary time series with one time-varying parameter. The driving force signals were also successfully extracted from the non-stationary time series with two time-varying parameters by SFA and wavelet transform technology. In addition, The driving force of Beijing air temperature was reconstructed by using SFA method. Wavelet transformation technique is 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.%慢特征分析法(Slow Feature Analysis, 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 方法能够较好地从单时变参数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.
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