document: A fuzzy-soft learning vector quantization for control chart pattern recognition This paper presents a supervised competitive learning network approach , called a fuzzy-soft learning vector quantization , for control chart pattern recognition . Unnatural patterns in control charts mean that there are some unnatural causes for variations in statistical process control -LRB- SPC -RRB- . Hence , control chart pattern recognition becomes more important in SPC. In order to detect effectively the patterns for the six main types of control charts , Pham and Oztemel -LRB- 1994 -RRB- described a class of pattern recognizers for control charts based on the learning vector quantization -LRB- LVQ -RRB- such as LVQ , LVQ2 and LVQ-X etc. . In this paper , we propose a new supervised LVQ for control charts based on a fuzzy-soft competitive learning network . The proposed fuzzy-soft LVQ -LRB- FS-LVQ -RRB- uses a fuzzy relaxation technique and simultaneously updates all neurons . It can increase correct recognition accuracy and also decrease the learning time . Comparisons between LVQ , LVQ-X and FS-LVQ are made
doc_bio_tags: O O O O O O B I I I O O O O B I I I I O O O O O O O O O B I I I O B I O O O O O O O O O O O O O B I I O B O O O O B I I I O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O B I I O O O O B I O O O O O O O O O O
extractive_keyphrases: control chart pattern recognition supervised competitive learning network approach unnatural patterns statistical process control spc supervised lvq fuzzy relaxation technique correct recognition accuracy learning time
abstractive_keyphrases: fuzzy-soft learning vector quantization simultaneous neuron update numerical results manufacturing process