非线性相关经典文献

wyljess

Nonlinear Canonical Correlation Analysis of the Tropical Pacific Climate Variability Using a Neural Network Approach

Recent advances in neural network modeling have led to the nonlinear generalization of classical multivariate
analysis techniques such as principal component analysis and canonical correlation analysis (CCA). The nonlinear
canonical correlation analysis (NLCCA) method is used to study the relationship between the tropical Pacific
sea level pressure (SLP) and sea surface temperature (SST) fields. The first mode extracted is a nonlinear El
Nin˜o–Southern Oscillation (ENSO) mode, showing the asymmetry between the warm El Nin˜ o states and the
cool La Nin˜a states. The nonlinearity of the first NLCCA mode is found to increase gradually with time. During
1950–75, the SLP showed no nonlinearity, while the SST revealed weak nonlinearity. During 1976–99, the SLP
displayed weak nonlinearity, while the weak nonlinearity in the SST was further enhanced. The second NLCCA
mode displays longer timescale fluctuations, again with weak, but noticeable, nonlinearity in the SST but not
in the SLP.

wyljess

初次发帖，不知道怎么添加附件，不好意思！！！

huzengyun

非线性研究一直是一个研究的热点，需要更多的人去做这个事情