Erratum to “Recognition of control chart patterns using multi-resolution wavelets analysis and neural networks” [Computers & Industrial Engineering 47 (2004) 17–29]

Manual detection of abnormality in control data is an annoying work which requires a specialized person. Automatic detection might be simpler and effective. Various methodologies such as ANN, SVM, Fuzzy Logic, etc. have been implemented into the control chart patterns to detect abnormal patterns in real time. In general, control chart data is imbalanced, meaning the rate of minority class (abnormal pattern) is much lower than the rate of normal class (normal pattern). To take this fact into consideration, authors implemented a weighting strategy in conjunction with ANN and investigated the performance of weighted ANN for several abnormal patterns, then compared its performance with regular ANN. This comparison is also made under different conditions, for example, abnormal and normal patterns are separable, partially separable, inseparable and the length of data is fixed as being 10,20, and 30 for each. Based on numerical results, weighting policy can better predict in some of the cases in terms of classifying samples belonging to minority class to the correct class.

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Recognition of control chart patterns using swarm intelligence and neural networks based on the statistical and shape features

IEEJ Transactions on Electrical and Electronic Engineering ◽

10.1002/tee.20610 ◽

2010 ◽

Vol 6 (1) ◽

pp. 80-85 ◽

Cited By ~ 1

Author(s):

Ataollah Ebrahimzadeh ◽

Vahid Ranaee

Keyword(s):

Neural Networks ◽

Swarm Intelligence ◽

Control Chart ◽

Shape Features ◽

Control Chart Patterns ◽

Chart Patterns

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An Assessment of Imbalanced Control Chart Pattern Recognition by Artificial Neural Networks

Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-0301-0.ch013 ◽

2020 ◽

pp. 240-258

Author(s):

Ramazan Ünlü

Keyword(s):

Neural Networks ◽

Fuzzy Logic ◽

Control Chart ◽

Minority Class ◽

Control Chart Patterns ◽

Abnormal Pattern ◽

Weighting Strategy ◽

Control Chart Pattern ◽

Chart Patterns ◽

Normal Class

Manual detection of abnormality in control data is an annoying work which requires a specialized person. Automatic detection might be simpler and effective. Various methodologies such as ANN, SVM, Fuzzy Logic, etc. have been implemented into the control chart patterns to detect abnormal patterns in real time. In general, control chart data is imbalanced, meaning the rate of minority class (abnormal pattern) is much lower than the rate of normal class (normal pattern). To take this fact into consideration, authors implemented a weighting strategy in conjunction with ANN and investigated the performance of weighted ANN for several abnormal patterns, then compared its performance with regular ANN. This comparison is also made under different conditions, for example, abnormal and normal patterns are separable, partially separable, inseparable and the length of data is fixed as being 10,20, and 30 for each. Based on numerical results, weighting policy can better predict in some of the cases in terms of classifying samples belonging to minority class to the correct class.

Download Full-text