Control Chart Pattern Recognition Using Optimized Adaptive Neuro- Fuzzy Inference System
محل انتشار: بیستمین کنفرانس مهندسی برق ایران
سال انتشار: 1391
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 1,511
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شناسه ملی سند علمی:
ICEE20_072
تاریخ نمایه سازی: 14 مرداد 1391
چکیده مقاله:
[Majid Masoumi] Electrical Engineering Department, Islamic Azad University Qazvin Branch, Qazvin, Iran
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 identifyingprocess problem. This paper presents a novel hybrid intelligent method for recognition of common types of control chartpatterns (CCP). The proposed method includes three main modules: a feature extraction module, a classifier module and an optimization module. In the feature extraction module, aproper set of the shape and statistical features are proposed as the efficient characteristic of the patterns. In the classifiermodule adaptive neuro-fuzzy inference system (ANFIS) is proposed that is a hybrid combination of artificial neural networks (ANN) and fuzzy inference system (FIS). In ANFIS training, the vector of radius has very important role for its recognition accuracy. Therefore, in the optimization module,bees algorithm (BA) is proposed for finding optimum vector of radius. Simulation results show that the proposed system hashigh recognition accuracy.
کلیدواژه ها:
نویسندگان
Reza Gholipour
Faculty of Electrical and Computer Engineering, Babol ( Noushirvani) University of Technology, Babol
Jalil Addeh
Faculty of Electrical and Computer Engineering, Babol ( Noushirvani) University of Technology, Babol
Ali Reza Sahab
Lahijan Branch Islamic Azad University
Samareh Fallah
Department of food science and technology, Faculty of Agriculture and Natural Resources, science and research Branch, Islamic Azad university, Tehran