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İSTATİSTİKSEL ÖZELLİK TEMELLİ BAYES SINIFLANDIRICI KULLANARAK KONTROL GRAFİKLERİNDE ÖRÜNTÜ TANIMA

CONTROL CHART PATTERN RECOGNITION USING STATISTICAL-FEATURE BASED BAYES CLASSIFIER

Journal Name:

  • Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

Publication Year:

  • 2012

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
Shewhart control charts for statistical process control are important tools to examination abnormal changes in a process. Artificial Neural Networks and Bayesian pattern recognition systems are formed to identify patterns of abnormal changes in a process to identify changes that may occur over time, to keep a process under control and to take necessary actions in a process. Classification performance of the generated pattern recognizers was measured. Six statistical features are issued from observations, that patterns were created, and classification performances were compared to improve the performance of correct classification. It is observed that Artificial Neural Networks and Bayesian pattern recognizers have higher performance after related features are defined. In conclusion, it is concluded that Bayesian pattern recognizer has better classification performance than artificial neural networks. Bayesian classifier can be used in real-time control charts for pattern recognition applications.
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Abstract (Original Language): 
İstatistiksel süreç kontrolünde kullanılan Shewhart kontrol grafikleri, süreçteki anormal değişimleri incelemede önemli bir araçtır. Süreçte zaman içinde oluşabilecek değişimlerin tespit edilmesi, sürecin kontrol altında tutulması ve önlemlerin alınması amacıyla süreçteki anormal değişimlerin örüntülerini tanımlamaya yönelik Yapay Sinir Ağları ve Bayes örüntü tanıma sistemleri oluşturulmuştur. Oluşturulan örüntü tanıyıcılarının sınıflandırma performansları ölçülmüştür. Doğru sınıflandırma performansını artırmak için örüntüleri oluşturan gözlem değerlerinden, altı adet istatistiksel özellik çıkarılmış ve sınıflandırma performansları karşılaştırılmıştır. yapay sinir ağları ve Bayes örüntü tanıyıcılarının, ilgili özellikler tanımlandıktan sonra daha yüksek performans verdiği görülmüştür. Sonuç olarak, Bayes örüntü tanıyıcının yapay sinir ağlarına nazaran daha iyi sınıflandırma performansının olduğu sonucuna varılmıştır. Bayes sınıflandırıcı gerçek zamanlı kontrol grafikleri uygulamalarında örüntü tanıma amaçlı kullanılabilir.
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