Autocorrelated Control Chart Patterns Recognition Using Support Vector Machine and Self-Organizing Map
碩士 === 國立雲林科技大學 === 工業工程與管理系 === 107 === Recognizing unnatural control chart patterns is an important issue. Many scholars used the most common method which is the neural network to recognize control patterns. The neural network has supervised learning network and unsupervised learning network. Both...
Main Authors: | HUANG,SHI-QI, 黃詩綺 |
---|---|
Other Authors: | Torng,Chau-Chen |
Format: | Others |
Language: | zh-TW |
Published: |
2019
|
Online Access: | http://ndltd.ncl.edu.tw/handle/fg22gb |
Similar Items
-
Recognition of Concurrent Control Chart Patterns in Autocorrelated Processes Using Support Vector Machine
by: Chau-Chen Torng, et al.
Published: (2016-08-01) -
Real-time Recognition of Control Chart Patterns in Autocorrelated Processes Using Support Vector Machine
by: Jo -Yu Lee, et al.
Published: (2009) -
The Study of Control Chart Pattern Recognition Using Support Vector Machine
by: Jun-Han Chen, et al.
Published: (2010) -
Applying Decision Tree in Pattern Recognition on Autocorrelated Multivariate Control Chart
by: Yu-Chun Liu, et al.
Published: (2011) -
Concurrent unnatural control chart patterns recognition of a bivariate process with support vector machine
by: Chong-Hao Huang, et al.
Published: (2015)