DocumentCode
2645925
Title
Comparison of Two Modern Pattern Recognition Methods
Author
Xiaochun Shi
Author_Institution
Dept. of Machine Eng., Dalian Jiaotong Univ., Dalian
fYear
2008
fDate
15-17 Aug. 2008
Firstpage
351
Lastpage
353
Abstract
Two methods of pattern recognition are introduced in this paper: Unsupervised learning algorithm - fuzzy clustering method and supervised learning algorithm - neural network. The pattern recognition becomes failure pattern recognition if it is used in the fault diagnosis of the machine. Both merits and shortages of these two methods are discussed through a specific example in the mechanical faults diagnosis.
Keywords
fuzzy set theory; neural nets; pattern recognition; unsupervised learning; fuzzy clustering method; mechanical faults diagnosis; neural network; pattern recognition; supervised learning algorithm; unsupervised learning algorithm; Clustering algorithms; Clustering methods; Fault diagnosis; Fuzzy neural networks; Intelligent networks; Learning systems; Neural networks; Pattern recognition; Signal processing algorithms; Unsupervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Information Hiding and Multimedia Signal Processing, 2008. IIHMSP '08 International Conference on
Conference_Location
Harbin
Print_ISBN
978-0-7695-3278-3
Type
conf
DOI
10.1109/IIH-MSP.2008.29
Filename
4604073
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