Somayeh Mirzaei

Work place: Shams University, Gonbad Kavous, Iran

E-mail: somayeh.mirzaei.shams@gmail.com

Website:

Research Interests: Autonomic Computing, Robotics, Data Structures and Algorithms

Biography

Somayeh Mirzaei was born in 1989, Iran. He is student of electrical engineering in Shams University, Gonbad Kavous, Iran. Her research interests include Soft computing, roboric and web. ID: somayeh.mirzaei.shams@gmail.com

Author Articles
Recognition of Control Chart Patterns Using Imperialist Competitive Algorithm and Fuzzy Rules Approach

By Somayeh Mirzaei Abdolhakim Nikpey Payam Zarbakhsh

DOI: https://doi.org/10.5815/ijisa.2014.10.09, Pub. Date: 8 Sep. 2014

Traditionally, Control Chart Patterns (CCP) is widely used as a powerful method to measure, classify,analyze and interpret process data to improve the quality of products and service by detecting instabilities and justifying possible causes. In this study, we have developed an expert system that we called an expert system for control chart patterns recognition for recognition of the common types of control chart patterns (CCPs). The proposed system includes three main modules: the feature extraction module, the classifier module and the optimization module. In the feature extraction module, the multi-resolution wavelets (MRW) are proposed as the effective features for representation of CCPs. In the classifier module, the adaptive neuro-fuzzy inference system (ANFIS) is investigated. In ANFIS training, the vector of radius has a very important role for its recognition accuracy. Therefore, in the optimization module, imperialist competitive algorithm(ICA) is proposed for finding optimum vector of radius. Simulation results show that the proposed system has high recognition accuracy.

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

By Abdolhakim Nikpey Somayeh Mirzaei Masoud Pourmandi Jalil Addeh

DOI: https://doi.org/10.5815/ijmecs.2014.07.03, 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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