Masoud Pourmandi

Work place: Ferdowsi University, Mashhad, Iran



Research Interests: Engineering


Massoud Pourmandi was born in 1989, Iran. He received BS degree in electronic engineering from the University of Guilan and M.Sc. student of Electrical Engineering- Communication Systems in Ferdowsi University of Mashhad.

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

By Abdolhakim Nikpey Somayeh Mirzaei Masoud Pourmandi Jalil Addeh

DOI:, Pub. Date: 8 Jul. 2014

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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