Recognition method based on principal component analysis and neural network

Computer Engineering and Applications ›› 2007, Vol. 43 ›› Issue (25): 246-248.

• 工程与应用 • Previous Articles

Recognition method based on principal component analysis and neural network

YANG Jing^1,2，MAO Zong-yuan¹

1.College of Automation Science and Engineering，South China University of Technology，Guangzhou 510640，China
2.School of Electrical and Electronic Engineering，East China Jiaotong University，Nanchang 330013，China

Received:1900-01-01 Revised:1900-01-01 Online:2007-09-01 Published:2007-09-01
Contact: YANG Jing

基于PCA和神经网络的识别方法研究

杨静^1,2，毛宗源¹

1.华南理工大学自动化科学与工程学院，广州 510460
2.华东交通大学电气与电子工程学院，南昌 330013

通讯作者: 杨静

Abstract

Abstract: Quality Control Charts is an important tool of Statistical Process Control（SPC） under the Contemporary Integrated Manufacturing System（CIMS） environment，and in practice it is most difficult to identify unnatural patterns which are associated with a specific set of assignable causes on Quality Control Charts.This paper discusses about control charts pattern，and then proposes intelligent recognition method based on principal component analysis and neural network.The principal component analysis is used to process the sample data.

Key words: Quality Control Charts, principal component analysis, neural network, pattern recognition

摘要： 在计算机集成制造系统环境下，质量控制图是统计过程控制的重要工具，实际应用中最困难的是识别出控制图中由于异常因素造成的不同异常模式。针对这一问题展开研究，用主成分分析法作为前处理过程进行样本集的选择与优化，提出了基于PCA_改进BP算法的控制图模式智能识别方法。

关键词: 质量控制图, 主成分分析, 神经网络, 模式识别

YANG Jing^1,2，MAO Zong-yuan¹. Recognition method based on principal component analysis and neural network [J]. Computer Engineering and Applications, 2007, 43(25): 246-248.

杨静^1,2，毛宗源¹. 基于PCA和神经网络的识别方法研究[J]. 计算机工程与应用, 2007, 43(25): 246-248.

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Recognition method based on principal component analysis and neural network

基于PCA和神经网络的识别方法研究

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