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Recognition of Mixture Control Chart Pattern Using Multiclass Support Vector Machine and Genetic Algorithm Based on Statistical and Shape Features
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-05
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Control charts have been widely utilized for monitoring process variation in numerous applications.
Abnormal patterns exhibited by control charts imply certain potentially assignable causes that may deteriorate the process performance.
Most of the previous studies are concerned with the recognition of single abnormal control chart patterns (CCPs).
This paper introduces an intelligent hybrid model for recognizing the mixture CCPs that includes three main aspects: feature extraction, classifier, and parameters optimization.
In the feature extraction, statistical and shape features of observation data are used in the data input to get the effective data for the classifier.
A multiclass support vector machine (MSVM) applies for recognizing the mixture CCPs.
Finally, genetic algorithm (GA) is utilized to optimize the MSVM classifier by searching the best values of the parameters of MSVM and kernel function.
The performance of the hybrid approach is evaluated by simulation experiments, and simulation results demonstrate that the proposed approach is able to effectively recognize mixture CCPs.
American Psychological Association (APA)
Zhang, Min& Cheng, Wenming. 2015. Recognition of Mixture Control Chart Pattern Using Multiclass Support Vector Machine and Genetic Algorithm Based on Statistical and Shape Features. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1073674
Modern Language Association (MLA)
Zhang, Min& Cheng, Wenming. Recognition of Mixture Control Chart Pattern Using Multiclass Support Vector Machine and Genetic Algorithm Based on Statistical and Shape Features. Mathematical Problems in Engineering No. 2015 (2015), pp.1-10.
https://search.emarefa.net/detail/BIM-1073674
American Medical Association (AMA)
Zhang, Min& Cheng, Wenming. Recognition of Mixture Control Chart Pattern Using Multiclass Support Vector Machine and Genetic Algorithm Based on Statistical and Shape Features. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1073674
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1073674