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A committee machine approach to multiple response optimization


Citation

Golestaneh, Seyed Jafar and Ismail, Napsiah and Tang, Sai Hong and Mohd Ariffin, Mohd Khairol Anuar and Naeini, Hassan Moslemi and Maghsoudi, Ali Asghar (2011) A committee machine approach to multiple response optimization. International Journal of the Physical Sciences, 6 (35). art. no. F8D798124547. pp. 7935-7949. ISSN 1992-1950

Abstract

Multiple responses optimization problems have three phases including design of experiments, modeling and optimization. Artificial neural networks and genetic algorithm are applied for second and third phases. Committee machines include some experts such as some neural networks which operate together to get response. Current article applies a committee machine including four different artificial neural networks to model multiple responses optimization problems. Genetic algorithm is applied to calculate weights of committee machine and also it optimizes desirability function of all responses to get optimum point. Seven different cases in multiple responses optimization were modeled and analyzed. The results show the error of committee machine is near half of average error of artificial neural networks and global desirability of committee machine is the same as average global desirability of artificial neural networks.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.5897/IJPS11.1469
Publisher: Academic Journals
Keywords: Committee machines; Multiple responses optimization; Genetic algorithm; Neural networks
Depositing User: Nabilah Mustapa
Date Deposited: 15 Apr 2020 16:20
Last Modified: 15 Apr 2020 16:20
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.5897/IJPS11.1469
URI: http://psasir.upm.edu.my/id/eprint/22877
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