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6This dataset was acquired in the Delft“Machine diagnostics by neural networks”-project with help from*Landustrie Sneek b v,The Netherlands* SKF Condition Monitoring b v,The Netherlands,and can be downloaded freely at the following web-address:http://www.ph.th.tudelft.nl/～ypma/mechanical.html(c)12-08-99.Pattern Recognition Group,Delft University of Technology.
3Juteen C,Herault J.Blind separation of sources,part 1:An adaptive algorithm based on neuromimetic structure.Signal processing,1991,24:1-10
4Nabil Charkani,Yannick Deville.Self-adaptive separation of convolutively mixed signals with a recursive structure.Part I:Stability analysis and optimization of asymptotic behaviour.Signal Processing,1999,73:225-254
5Hoang-Lan Nguyen Thi,Christian Jutten.Blind source separation for convolution mixtures.Signal Processing,1995,45:209-229
6Gelle G,Colas M,Delaunay G.Blind soures separation applied to rotating machines monitoring by acoustical and vibrations analysis.Mechanical Systems and Signal Processing,2000,14(3):427-442
7G.GeUe, M. Colas and G. Delaunay. Blind sources separation applied to rotating machines monitoring by acoustical and vibrations analysis [J]. Mechanical Systems and Signal Processing 2000, 14(3): 427-442.
8R.Linsker. Local synaphc leaming rules suffice to maximize mutual information in a linear network[J]. Neural Computation, 1992, 4(3): 491-702.
