This paper describes the use of unsupervised adaptive resonance theory ART2 neural networks for recognizing patterns in statistical process control charts. To improve the classi® cation accuracy, three schemes are proposed. The ® rst scheme involves using information on changes between consecutive points in a pattern. The second scheme modi® es the ART2 vigilance parameter during training. The ...

This paper presents a control chart pattern recognition system using a statistical correlation coefficient method. Pattern recognition techniques have been widely applied to identify unnatural patterns in control charts. Most of them are capable of recognizing a single unnatural pattern for different abnormal types. However, before an unnatural pattern occurs, a change point from normal to abno...

Control chart pattern recognition has become an active area of research since late 1980s. Much progress has been made, in which there are trends to heighten the performance of artificial neural network (ANN)-based control chart pattern recognition schemes through feature-based and wavelet-denoise input representation techniques, and through modular and integrated recognizer designs. There is al...

the electronic industry suffers a rapid changing and highly rival environment. thus, firms have an essential need to strive for acquiring the competitive advantage. strategy organizational agility (soa) is a tool which enables to assist firms to attain the competitive advantage. therefore, this study benchmarks the core competencies from a case study within the supply chain network and establis...

The continuous detection and correction of unnatural process behaviours, due to special causes of variations, is a basic task in manufacturing to maintain any process stable and predictable. For this purpose, updated tools for Statistical Process Control have been studied so far, like the use of Artificial Neural Networks for pattern recognition in control charts. In this paper a preliminary st...

The identification of control chart patterns is very important in statistical process control. Control chart patterns are categorized as natural and unnatural. The presence of unnatural patterns means that a process is out of statistical control and there are assignable causes for process variation that should be investigated. This paper proposes an artificial neural network algorithm to identi...

Feature extraction is a crucial part of classijicationprocedures. In this paper we present an approach, how to utilize feature extraction criteria to predict the potential efficiency of a neural network classijiel: Statistical and geometrical criteria are introduced for analysis. The complete system of our research consists of a class of generalized Hough-Transformations for feature extraction ...