Title :
Improved kernel density estimation for clustered data using regularisation and deconvolution
Author :
Chen, Q. ; Sandoz, D. ; Wynne, R.J. ; Kruger, U.
Author_Institution :
Manchester Univ., UK
Abstract :
To extract multivariate probability density functions (PDF) from a clustered training data set for condition monitoring purposes, a modified kernel density estimation method is suggested using regularisation and deconvolution techniques. Case studies show that it is a useful pragmatic method for real industrial data
Keywords :
condition monitoring; deconvolution; probability; process monitoring; clustered data; condition monitoring; deconvolution; kernel density estimation; multivariate probability density functions; regularisation; Bandwidth; Condition monitoring; Data mining; Deconvolution; Density functional theory; Kernel; Neural networks; Noise level; Statistics; Training data;
Conference_Titel :
American Control Conference, 2000. Proceedings of the 2000
Conference_Location :
Chicago, IL
Print_ISBN :
0-7803-5519-9
DOI :
10.1109/ACC.2000.876733
