Title :
A combined self-organizing map neural network with analysis graphical approach for mixed-weibull parameter estimation
Author :
Lee, Pei-Hsi ; Torng, Chau-Chen
Author_Institution :
Grad. Sch. of Ind. Eng. & Manage., Nat. Yunlin Univ. of Sci. & Technol., Douliou, Taiwan
Abstract :
The mixed-Weibull distribution is widely used to analyze the burn-in time. Kececioglu had presented its parameter estimation method with application of Weibull probability plot (WPP) such a graphic analysis method. However his method is not easy to estimate parameters when the data loses the failure mode information. A self-organizing map neural network (SOM) is used to cluster the classification of failure mode. We combined SOM with Kececioglu¿s method to estimate the parameters of mixed-Weibull distribution. Some simulation studies are given to present the accuracy of parameter estimation of our method under small sample size.
Keywords :
Weibull distribution; graph theory; parameter estimation; pattern classification; pattern clustering; self-organising feature maps; Weibull probability plot; analysis graphical approach; failure mode information classification; mixed-Weibull parameter estimation; pattern clustering; self-organizing map neural network; Data analysis; Engineering management; Graphics; Industrial engineering; Life testing; Maximum likelihood estimation; Neural networks; Parameter estimation; Technology management; Weibull distribution; Mixed-weibull distribution; Self-organizing map neural network; Weibull probability plot;
Conference_Titel :
Industrial Engineering and Engineering Management, 2008. IEEM 2008. IEEE International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-2629-4
Electronic_ISBN :
978-1-4244-2630-0
DOI :
10.1109/IEEM.2008.4738094
