Control Chart Patterns Recognition Using Fuzzy Rules and Efficient Features

Publish Year: 1394
نوع سند: مقاله کنفرانسی
زبان: English
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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

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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