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

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

Автор: Abdolhakim Nikpey, Somayeh Mirzaei, Masoud Pourmandi, Jalil Addeh

Журнал: International Journal of Modern Education and Computer Science (IJMECS) @ijmecs

Статья в выпуске: 7 vol.6, 2014 года.

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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.

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ANFIS, Control chart patterns, Shape features, Statistical feature, COA

Короткий адрес: https://sciup.org/15014668

IDR: 15014668

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