in this research, an iterative approach is employed to recognize and classify control chart patterns. to do this, by taking new observations on the quality characteristic under consideration, the maximum likelihood estimator of pattern parameters is first obtained and then the probability of each pattern is determined. then using bayes’ rule, probabilities are updated recursively. finally, when...

Dealing with uncertainty is one of the most critical problems in complicatedpattern recognition subjects. In this paper, we modify the structure of a useful UnsupervisedFuzzy Neural Network (UFNN) of Kwan and Cai, and compose a new FNN with 6 types offuzzy neurons and its associated self organizing supervised learning algorithm. Thisimproved five-layer feed forward Supervised Fuzzy Neural Netwo...

This paper proposes two alternative schemes for the online recognition of control chart patterns (CCPs), namely: 1 a scheme based on direct continuous recognition 2 a scheme based on ‘recognition only when necessary’. The study focuses on recognition of six CCPs plotted on the Shewhart X-bar chart, namely, random, shift-up, shift down, trend-up, trend-down and cyclic. The artificial neural netw...

In this paper we propose the use of statistical features for time-series classi cation. The classi cation is performed with a multi-layer perceptron (MLP) neural network. The proposed method is examined in the context of Control Chart Pattern data, which are time series used in Statistical Process Control. Experimental results verify the e ciency of the feature-based classi cation method, compa...

The identification or interpretation of the pattern in an image can be described effectively with the help of Pattern Recognition (PR). It aims to extract information about the image to classify its contents. Inputs are in the form of digitized binary valued 2D images or textures containing the pattern to be classified. The analysis and recognition of the patterns such as images and textures ar...

Control Chart Pattern Recognition (CCPR) is a critical task in Statistical Process Control (SPC). Abnormal patterns exhibited in control charts can be associated with certain assignable causes adversely affecting the process stability. Abundant literature treats the detection of different Control Chart Patterns (CCPs). In fact, numerous CCPR studies have been developed according to various obje...