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本帖最后由 hillside 于 2013-12-22 16:25 编辑
以下介绍加拿大不列颠哥伦比亚大学的非线性典型相关分析(NLCCA)与非线性奇异谱分析(NLSSA)及非线性主成分(NLPCA)兼收并蓄的集成程序包Neuralnets for Multivariate And Time Series Analysis (NeuMATSA), 这三种方法在《气候变率诊断和预测方法》(吴洪宝等著)中均有介绍 。有兴趣者可点击本帖页底表格申请下载软件。
http://www.ocgy.ubc.ca/projects/clim.pred/download.html
Neuralnets for Multivariate And Time Series Analysis (NeuMATSA)
MATLAB codes for:Nonlinear Principal Component Analysis (NLPCA)
Nonlinear Canonical Correlation Analysis (NLCCA) (**** Bug report (2008/1/27) **** ).
Nonlinear Singular Spectrum Analysis (NLSSA)
The latest release (version 5.0) became available in Oct. 2007. [The major improvements over the previous version are: (1) The appropriate weight penalty parameters are now objectively determined by the codes. (2) Robust options have been introduced in the codes to handle noisy datasets containing outliers.]The programs are free software, under the terms of the GNU General Public License as published by the Free Software Foundation. The codes are written inMATLAB and use its Optimization Toolbox.
First download the manual:
Hsieh, W.W., 2008. Neuralnets for Multivariate And Time Series Analysis (NeuMATSA): A User Manual (in PDF format).
Next download the 2004 general review paper and more recent paper(s) of relevance:
Hsieh, W.W., 2004. Nonlinear multivariate and time series analysis by neural network methods. Reviews of Geophysics, 42, RG1003, doi:10.1029/2002RG000112. (reprint with typos corrected in PDF)
Hsieh, W.W., 2007. Nonlinear principal component analysis of noisy data. Neural Networks, 20: 434-443. DOI 10.1016/j.neunet.2007.04.018. (preprint in PDF)
Cannon, A.J. and W.W. Hsieh, 2008. Robust nonlinear canonical correlation analysis: application to seasonal climate forecasting. Nonlinear Processes in Geophysics, 15: 221-232. (preprint in PDF)
Some of the papers written by our group referenced in this review paper can be downloaded at the site http://www.ocgy.ubc.ca/~william/pubs.html.
For Nonlinear Complex Principal Component Analysis (NLCPCA), Sanjay Rattan (e-mail: "srattan" followed by "@ualberta.ca") converted the NLPCA code to complex variables. There is no written manual other than a file manual.m attached to the Matlab codes. The relevant publication is:
Rattan, S.S.P. and Hsieh, W.W., 2005. Complex-valued neural networks for nonlinear complex principal component analysis. Neural Networks, 18: 61-69, DOI:10.1016/j.neunet.2004.08.002. (preprint in PDF).
Finally fill out a registration form, so we can notify you of any problems or upgrades: Fill registration form.Once the registration form is completed you will be able to download the codes.
以下为我提交申请表之后,页面自动转为文件可下载状态。申请非常方便,有如即开型彩票。
Data AcceptedMailing list subscriptionInformation regarding your subscription to 'registrant_email@eos.ubc.ca' will be sent to the email address ……@………….Mailing list subscription failed, could not connect to mail serverSend email to majordomo@eos.ubc.ca with the body
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Finally download the codes:(These files are in tar format. Use the unix tar command to decompress them, each expanding out to a directory of files.)
Latest release (version 5.0):
NLPCA (Nonlinear Principal Component Analysis) (version 5.0)
NLPCA.cir (Nonlinear Principal Component Analysis with a Circular bottleneck neuron) (version 5.0) [also used for nonlinear singular spectrum analysis]
NLCCA (Nonlinear Canonical Correlation Analysis) (version 5.0)
NLCPCA (Nonlinear Complex Principal Component Analysis) (version 2004.1) [contributed by Sanjay Rattan]
Previous release (version 3.1):
Version 3.1 is compatible with MATLAB 7. However, some of the function M-files available in Matlab 6 and 6.5 have been dropped in MATLAB 7. So if you are running on MATLAB 7, you probably need to download Missing-M-files.
NLPCA (Nonlinear Principal Component Analysis) (version 3.1)
NLPCA.cir (Nonlinear Principal Component Analysis with a Circular bottleneck neuron) (version 3.1) [also used for nonlinear singular spectrum analysis]
NLCCA (Nonlinear Canonical Correlation Analysis) (version 3.1)
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