Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If You Use a Screen Reader

This content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.

Intelligent Recognition of Mixture Control Chart Pattern Based on Quadratic Feature Extraction and SVM with AMPSO

Min Zhang, Wenming Cheng and Peng Guo
Journal of Coastal Research
Special Issue No. 73. Recent Developments on Port and Ocean Engineering (Winter 2015), pp. 304-309
Stable URL: http://www.jstor.org/stable/43843283
Page Count: 6
  • Read Online (Free)
  • Download ($20.00)
  • Subscribe ($19.50)
  • Cite this Item
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
Preview not available

Abstract

Take control charts as the principal statistical process control tools. The recognition accuracy of abnormal control chart patterns (CCPs) directly influences the quality control of production process. (Most of the existing studies focus on the basic recognition of abnormal CCPs, but the observed process data could be of mixture patterns in real world, which consists of two or three basic patterns. This paper introduces a hybrid intelligent model for recognizing the mixture control chart pattern that includes three main aspects: feature extraction, classifier and parameter optimization. In feature extraction, statistical and sharp features of observation data are used as data input and principal component analysis as the second feature extraction to get the effective data of the classifier. A multi-class support vector machine (SVM) is applied for recognizing the mixture patterns. Finally, adaptive mutation particle swam optimization is used to optimize the SVM classifier by searching the best values of the parameters of SVM kernel function. The simulation results show the proposed algorithm with little features has better recognition accuracy compared with other methods.

Page Thumbnails

  • Thumbnail: Page 
[304]
    [304]
  • Thumbnail: Page 
305
    305
  • Thumbnail: Page 
306
    306
  • Thumbnail: Page 
307
    307
  • Thumbnail: Page 
308
    308
  • Thumbnail: Page 
309
    309