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A Robust Intelligent Framework for Multiple Response Statistical Optimization Problems Based on Artificial Neural Network and Taguchi Method
المؤلفون المشاركون
Moeini, Asghar
Bastan, Mahdi
Salmasnia, Ali
المصدر
International Journal of Quality, Statistics, and Reliability
العدد
المجلد 2012، العدد 2012 (31 ديسمبر/كانون الأول 2012)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2012-07-26
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
العلوم الاقتصادية والمالية وإدارة الأعمال
الاقتصاد
الملخص EN
An important problem encountered in product or process design is the setting of process variables to meet a required specification of quality characteristics (response variables), called a multiple response optimization (MRO) problem.
Common optimization approaches often begin with estimating the relationship between the response variable with the process variables.
Among these methods, response surface methodology (RSM), due to simplicity, has attracted most attention in recent years.
However, in many manufacturing cases, on one hand, the relationship between the response variables with respect to the process variables is far too complex to be efficiently estimated; on the other hand, solving such an optimization problem with accurate techniques is associated with problem.
Alternative approach presented in this paper is to use artificial neural network to estimate response functions and meet heuristic algorithms in process optimization.
In addition, the proposed approach uses the Taguchi robust parameter design to overcome the common limitation of the existing multiple response approaches, which typically ignore the dispersion effect of the responses.
The paper presents a case study to illustrate the effectiveness of the proposed intelligent framework for tackling multiple response optimization problems.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Salmasnia, Ali& Bastan, Mahdi& Moeini, Asghar. 2012. A Robust Intelligent Framework for Multiple Response Statistical Optimization Problems Based on Artificial Neural Network and Taguchi Method. International Journal of Quality, Statistics, and Reliability،Vol. 2012, no. 2012, pp.1-11.
https://search.emarefa.net/detail/BIM-476173
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Salmasnia, Ali…[et al.]. A Robust Intelligent Framework for Multiple Response Statistical Optimization Problems Based on Artificial Neural Network and Taguchi Method. International Journal of Quality, Statistics, and Reliability No. 2012 (2012), pp.1-11.
https://search.emarefa.net/detail/BIM-476173
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Salmasnia, Ali& Bastan, Mahdi& Moeini, Asghar. A Robust Intelligent Framework for Multiple Response Statistical Optimization Problems Based on Artificial Neural Network and Taguchi Method. International Journal of Quality, Statistics, and Reliability. 2012. Vol. 2012, no. 2012, pp.1-11.
https://search.emarefa.net/detail/BIM-476173
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-476173
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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