2011
DOI: 10.1016/j.cie.2010.12.024
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Monitoring roundness profiles based on an unsupervised neural network algorithm

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Cited by 27 publications
(11 citation statements)
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“…Pacella and Semeraro extended the study of Al‐Ghanim by proposing a fuzzy ART neural network to address arbitrary sequences of input patterns, whereas the ANN model proposed by Al‐Ghanim was limited to binary inputs. Pacella and Semeraro derived a variant of the previously proposed fuzzy‐ART‐based scheme to monitor the stability over time of profile data. A different type of unsupervised ANN method, that is, the self‐organizing map, was discussed by Tax for one‐class‐classification applications.…”
Section: A Rationale For the Use Of One‐class‐classification Methodsmentioning
confidence: 99%
See 2 more Smart Citations
“…Pacella and Semeraro extended the study of Al‐Ghanim by proposing a fuzzy ART neural network to address arbitrary sequences of input patterns, whereas the ANN model proposed by Al‐Ghanim was limited to binary inputs. Pacella and Semeraro derived a variant of the previously proposed fuzzy‐ART‐based scheme to monitor the stability over time of profile data. A different type of unsupervised ANN method, that is, the self‐organizing map, was discussed by Tax for one‐class‐classification applications.…”
Section: A Rationale For the Use Of One‐class‐classification Methodsmentioning
confidence: 99%
“…Pacella and Semeraro proposed a method used to select the proper value of the ρ parameter given a targeted Type I error. In the presence of multimode data, the fuzzy ART network could be theoretically trained to find the most appropriate clustering configuration of IC variables.…”
Section: A Quality Control Scheme Based On Fuzzy Art Networkmentioning
confidence: 99%
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“…Wang and Guo presented the process yield for nonlinear profiles. Recently, roundness profile monitoring such as circle and cylindrical profiles is considered by some researchers . In order to identify a signature model, linear regression is often used.…”
Section: Introductionmentioning
confidence: 99%
“…Colosimo et al . used a spatial autoregressive regression to model circular profile, and Pacella and Semeraro proposed an unsupervised neural network in monitoring circular profile.…”
Section: Introductionmentioning
confidence: 99%