Recognition of control chart patterns using an intelligent technique
| Autor: | Ata Ebrahimzadeh, Jalil Addeh, Vahid Ranaee |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Artificial neural network
business.industry Computer science Feature extraction Pattern recognition Statistical process control computer.software_genre Support vector machine ComputingMethodologies_PATTERNRECOGNITION Feature (machine learning) Control chart Artificial intelligence Data mining business Classifier (UML) computer Software Bees algorithm |
| Zdroj: | Applied Soft Computing. 13:2970-2980 |
| ISSN: | 1568-4946 |
| Popis: | Control chart patterns (CCPs) are important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. 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 CCP. The proposed method includes three main modules: the feature extraction module, the classifier module and 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 multilayer perceptron neural network and support vector machine (SVM) are investigated. In support vector machine training, the hyper-parameters have very important roles for its recognition accuracy. Therefore, in the optimization module, improved bees algorithm is proposed for selecting of appropriate parameters of the classifier. Simulation results show that the proposed algorithm has very high recognition accuracy. |
| Databáze: | OpenAIRE |
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