Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process
The effective controlling and monitoring of an industrial process through the integration of statistical process control (SPC) and engineering process control (EPC) has been widely addressed in recent years. However, because the mixture types of disturbances are often embedded in underlying processe...
Main Authors: | Yuehjen E. Shao, Po-Yu Chang, Chi-Jie Lu |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi-Wiley
2017-01-01
|
Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2017/2323082 |
Similar Items
-
Applying Two-stage Computational Intelligence Approaches to the Identification of Mixture Types of Control Chart for an SPC/EPC Process
by: Chang, Po-Yu, et al.
Published: (2017) -
Recognition of Process Disturbances for an SPC/EPC Stochastic System Using Support Vector Machine and Artificial Neural Network Approaches
by: Yuehjen E. Shao
Published: (2014-01-01) -
The Evaluation and Selection of Control Charts for Integrated SPC and EPC
by: Tsung-Ju Yang, et al.
Published: (2000) -
Application of Machine Learning Approaches to the Recognition of Control Chart Patterns for an SPC-EPC Process
by: Wang, Yi-Hsieh, et al.
Published: (2015) -
Applying Time Delay Neural Network to Construct an Integrated SPC-EPC System
by: Kuo-Chang Wu, et al.
Published: (2006)