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Recognition of Process Disturbances for an SPCEPC Stochastic System Using Support Vector Machine and Artificial Neural Network Approaches
المؤلف
المصدر
العدد
المجلد 2014، العدد 2014 (31 ديسمبر/كانون الأول 2014)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2014-06-09
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Because of the excellent performance on monitoring and controlling an autocorrelated process, the integration of statistical process control (SPC) and engineering process control (EPC) has drawn considerable attention in recent years.
Both theoretical and empirical findings have suggested that the integration of SPC and EPC can be an effective way to improve the quality of a process, especially when the underlying process is autocorrelated.
However, because EPC compensates for the effects of underlying disturbances, the disturbance patterns are embedded and hard to be recognized.
Effective recognition of disturbance patterns is a very important issue for process improvement since disturbance patterns would be associated with certain assignable causes which affect the process.
In practical situations, after compensating by EPC, the underlying disturbance patterns could be of any mixture types which are totally different from the original patterns.
This study proposes the integration of support vector machine (SVM) and artificial neural network (ANN) approaches to recognize the disturbance patterns of the underlying disturbances.
Experimental results revealed that the proposed schemes are able to effectively recognize various disturbance patterns of an SPC/EPC system.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Shao, Yuehjen E.. 2014. Recognition of Process Disturbances for an SPCEPC Stochastic System Using Support Vector Machine and Artificial Neural Network Approaches. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1014154
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Shao, Yuehjen E.. Recognition of Process Disturbances for an SPCEPC Stochastic System Using Support Vector Machine and Artificial Neural Network Approaches. Abstract and Applied Analysis No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-1014154
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Shao, Yuehjen E.. Recognition of Process Disturbances for an SPCEPC Stochastic System Using Support Vector Machine and Artificial Neural Network Approaches. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1014154
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1014154
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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