IJE TRANSACTIONS C: Aspects Vol. 30, No. 9 (September 2017) 1372-1380   

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M. Kabiri Naeini and N. Bayati
( Received: January 21, 2017 – Accepted in Revised Form: July 07, 2017 )

Abstract    Today for the expedition of the identification and timely correction of process deviations, it is necessary to use advanced techniques to minimize the costs of production of defective products. In this way control charts as one of the important tools for the statistical process control in combination with modern tools such as artificial neural networks have been used. The artificial neural networks were used to recognize the pattern in control charts in several research. Two procedures were used based on the raw data and feature for training and application of neural network. This paper presented new statistical features besides the investigation of their efficiency by application of a neural network. The simulation results demonstrated the positive effect of the presented statistical feature on neural network performance.


Keywords    Control chart. Pattern recognition. Neural network. Statistical feature


چکیده    امروزه برای تسریع در شناسایی انحرافات فرایند و اصلاح به­موقع آن لازم است از فنون پیشرفته­ای استفاده شود تا هزینه­های ناشی از تولید محصولات معیوب به حداقل برسد. در این مسیر، نمودارکنترل به عنوان یکی از ابزارهای مهم کنترل آماری فرایند در ترکیب با ابزارهای مدرن همچون شبکه­های عصبی مصنوعی مورد استفاده قرار گرفته است. شبکه­های عصبی مصنوعی برای تشخیص الگو در نمودارهای کنترل در پژوهش­های مختلف مورد استفاده قرار گرفته­اند. برای آموزش و کاربرد شبکه عصبی مصنوعی در تشخیص الگو، از دو رویکرد مبتنی بر داده خام و مبتنی بر ویژگی استفاده شده است. در این مقاله ضمن معرفی ویژگی­های آماری جدید، کارایی آن در تشخیص الگو با استفاده از شبکه عصبی مطالعه شده استو نتایج شبیه­سازی تاثیر مثبت ویژگی آماری ارائه شده را بر عملکرد شبکه عصبی نشان می­دهد.


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