Synthetic neural networks for process control
Computers and Industrial Engineering
Back-propagation pattern recognizers for X¯ control charts: methodology and performance
Computers and Industrial Engineering
Back propagation artificial neural networks for the analysis of quality control charts
Proceedings of the 15th annual conference on Computers and industrial engineering
Out-of-control pattern recognition and analysis for quality control charts using LISP-based systems
Computers and Industrial Engineering
A neural network based model for abnormal pattern recognition of control charts
Computers and Industrial Engineering
A mixed integer optimisation model for data classification
Computers and Industrial Engineering
Neural networks for detecting cyclic behavior in autocorrelated process
Computers and Industrial Engineering
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In quality control discipline, pattern classification is focused on the detection of unnatural patterns in process data. In this paper, fractal dimension is proposed as a new classifier for pattern classification. Fractal dimension is an index for measuring the complexity of an object. Its applications were found in such diverse fields as manufacturing, material science, medical, and image processing. A method for detecting patterns in process data using the fractal dimension is proposed in this paper. A Monte Carlo study was carried out to study the fractal dimension (D) and the Y-intercept (Yint) values of process data with patterns of interest. The patterns is included in the study are natural pattern, upward linear trend, downward linear trend, cycle, systematic variable stratification, mixture, upward sudden shift, and downward sudden shift. Based on the results, the approach is effective in detecting such non-periodic patterns as the natural patterns, linear trends (at slope ≥ 0.2), systematic variable, stratification, mixture, and sudden shifts. For the cyclical pattern, although the D and Yint-values are not stable, the approach can provide useful information when the period of the cycle is greater than 2 and is less than or equal to half the window size (2 N/2). The minor drawbacks of this approach are that it is not sensitive for detecting linear trends with small slope and the slope of the original data is needed to detect the difference between upward and downward linear trends and the difference between upward and downward sudden shifts.