Control Chart Patterns Recognition Using Fuzzy Rules and Efficient Features
Publish place: International Conference on New Research Findings in Electrical Engineering and Computer Science
Publish Year: 1394
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
COMCONF01_633
تاریخ نمایه سازی: 8 آذر 1394
Abstract:
Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in the manufacturing processes. 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 are 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, particle swarm optimization (PSO) algorithm is proposed for finding the optimum vector of radius. Simulation results show that the proposed system has high recognition accuracy
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Authors
Hossein Babaee
Babol Noshirvani University
Ali Lari
Babol Noshirvani University
Javad Ganjipour
Babol Noshirvani University
Jalil Addeh
Babol Noshirvani University
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