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

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Abdolhakim Nikpey 1,* Somayeh Mirzaei 1 Masoud Pourmandi 2 Jalil Addeh 3

1. Shams University, Gonbad Kavous, Iran

2. Ferdowsi University, Mashhad, Iran

3. Payam Noor University, Gonbad Kavous, Iran

* Corresponding author.


Received: 15 Mar. 2014 / Revised: 10 Apr. 2014 / Accepted: 26 May 2014 / Published: 8 Jul. 2014

Index Terms

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


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.

Cite This Paper

Abdolhakim Nikpey, Somayeh Mirzaei, Masoud Pourmandi, JalilAddeh, "Identification of the Control Chart Patterns Using the Optimized Adaptive Neuro-Fuzzy Inference System", International Journal of Modern Education and Computer Science (IJMECS), vol.6, no.7, pp.16-24, 2014. DOI:10.5815/ijmecs.2014.07.03


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