A Study of Pattern Recognition of Statistical Control Chart Based on Random Forest

doi:10.3969/j.issn.1007-7375.2019.05.015

Abstract

Abstract: The random forest method is introduced to study the pattern recognition of statistical control chart. The statistical features and shape features of the control graph were extracted, five different feature combination methods were designed, and Monte-carlo simulation method was used to generate training data set and test data set. Three commonly used pattern recognition methods (support vector machine method, artificial neural network method and decision tree method) were selected for comparison. Experimental results show that compared with the other three classifier methods, the random forest method has obvious advantages in classification accuracy and consumption time, and can be applied to the pattern recognition of statistical process control chart.

Key words: statistical control chart, pattern recognition, support vector machine, class and regression tree, artificial neural network, random forrest

CLC Number:

TP391.4

WANG Haiyan, HOU Linna. A Study of Pattern Recognition of Statistical Control Chart Based on Random Forest[J]. Industrial Engineering Journal , 2019, 22(5): 118-125.

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