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Desirability Improvement of Committee Machine to Solve Multiple Response Optimization Problems
Joint Authors
Ismail, Napsiah
Golestaneh, Seyed Jafar
Sai Hong, Tang
Moslemi Naeini, Hassan
Ariffin, Mohd Khairol Anuar M.
Source
Advances in Artificial Neural Systems
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-09-16
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
Multiple response optimization (MRO) problems are usually solved in three phases that include experiment design, modeling, and optimization.
Committee machine (CM) as a set of some experts such as some artificial neural networks (ANNs) is used for modeling phase.
Also, the optimization phase is done with different optimization techniques such as genetic algorithm (GA).
The current paper is a development of recent authors' work on application of CM in MRO problem solving.
In the modeling phase, the CM weights are determined with GA in which its fitness function is minimizing the RMSE.
Then, in the optimization phase, the GA specifies the final response with the object to maximize the global desirability.
Due to the fact that GA has a stochastic nature, it usually finds the response points near to optimum.
Therefore, the performance the algorithm for several times will yield different responses with different GD values.
This study includes a committee machine with four different ANNs.
The algorithm was implemented on five case studies and the results represent for selected cases, when number of performances is equal to five, increasing in maximum GD with respect to average value of GD will be eleven percent.
Increasing repeat number from five to forty-five will raise the maximum GD by only about three percent more.
Consequently, the economic run number of the algorithm is five.
American Psychological Association (APA)
Golestaneh, Seyed Jafar& Ismail, Napsiah& Ariffin, Mohd Khairol Anuar M.& Sai Hong, Tang& Moslemi Naeini, Hassan. 2013. Desirability Improvement of Committee Machine to Solve Multiple Response Optimization Problems. Advances in Artificial Neural Systems،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-486376
Modern Language Association (MLA)
Golestaneh, Seyed Jafar…[et al.]. Desirability Improvement of Committee Machine to Solve Multiple Response Optimization Problems. Advances in Artificial Neural Systems No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-486376
American Medical Association (AMA)
Golestaneh, Seyed Jafar& Ismail, Napsiah& Ariffin, Mohd Khairol Anuar M.& Sai Hong, Tang& Moslemi Naeini, Hassan. Desirability Improvement of Committee Machine to Solve Multiple Response Optimization Problems. Advances in Artificial Neural Systems. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-486376
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-486376