X-BAR AND R-CONTROL CHART INTERPRETATION USING NEURAL COMPUTING

被引：107

作者：

SMITH, AE

机构：

[1] Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, PA, 15261

来源：

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH | 1994年 / 32卷 / 02期

关键词：

Artificial intelligence - Data processing - Neural networks - Probability - Production control;

D O I：

10.1080/00207549408956935

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper formulates Shewhart mean (X-bar) and range (R) control charts for diagnosis and interpretation by artificial neural networks. Neural networks are trained to discriminate between samples from probability distributions considered within control limits and those which have shifted in both location and variance. Neural networks are also trained to recognize samples and to predict future points from processes which exhibit long-term or cyclical drift. The advantages and disadvantages of neural control charts compared with traditional statistical process control are discussed.

引用

页码：309 / 320

页数：12

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