Identification of the Control Chart Patterns Using the Optimized Adaptive Neuro-Fuzzy Inference System

Unnatural patterns in the control charts can be associated with a specific set of assignable causes for process variation. Hence pattern recognition is very useful in identifying process problem. This paper presents a novel hybrid intelligent method for recognition of common types of control chart p...

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Bibliographic Details
Published in:International journal of modern education and computer science 2014-07, Vol.6 (7), p.16-24
Main Authors: Nikpey, Abdolhakim, Mirzaei, Somayeh, Pourmandi, Masoud, Addeh, Jalil
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Charts
Computer Science
Control charts
Fuzzy logic
Inferences
Networks
Optimization algorithms
Pattern recognition
Statistical Analysis
Statistical Distributions
Topology
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Summary:Unnatural patterns in the control charts can be associated with a specific set of assignable causes for process variation. Hence pattern recognition is very useful in identifying process problem. This paper presents a novel hybrid intelligent method for recognition of common types of control chart patterns (CCPs). The proposed method includes three main modules: the feature extraction module, the classifier module and the optimization module. In the feature extraction module, a proper set of the shape features and statistical features is proposed as the efficient characteristic of the patterns. In the classifier module adaptive neuro-fuzzy inference system (ANFIS) is investigated. In ANFIS training, the vector of radius has very important role for its recognition accuracy. Therefore, in the optimization module, cuckoo optimization algorithm (COA) is proposed for finding of optimum vector of radius. Simulation results show that the proposed system has high recognition accuracy.
ISSN:2075-0161
2075-017X
DOI:10.5815/ijmecs.2014.07.03