A committee machine approach to multiple response optimization
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Academic Journals
2011
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| Online Access: | http://psasir.upm.edu.my/id/eprint/22877/1/22877.pdf |
| _version_ | 1825925243067170816 |
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| author | Golestaneh, Seyed Jafar Ismail, Napsiah Tang, Sai Hong Mohd Ariffin, Mohd Khairol Anuar Naeini, Hassan Moslemi Maghsoudi, Ali Asghar |
| author_facet | Golestaneh, Seyed Jafar Ismail, Napsiah Tang, Sai Hong Mohd Ariffin, Mohd Khairol Anuar Naeini, Hassan Moslemi Maghsoudi, Ali Asghar |
| author_sort | Golestaneh, Seyed Jafar |
| collection | UPM |
| description | 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. |
| first_indexed | 2024-03-06T07:55:15Z |
| format | Article |
| id | upm.eprints-22877 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T07:55:15Z |
| publishDate | 2011 |
| publisher | Academic Journals |
| record_format | dspace |
| spelling | upm.eprints-228772020-04-15T16:20:56Z http://psasir.upm.edu.my/id/eprint/22877/ A committee machine approach to multiple response optimization Golestaneh, Seyed Jafar Ismail, Napsiah Tang, Sai Hong Mohd Ariffin, Mohd Khairol Anuar Naeini, Hassan Moslemi Maghsoudi, Ali Asghar 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. Academic Journals 2011 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/22877/1/22877.pdf 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 https://academicjournals.org/journal/IJPS/article-abstract/F8D798124547 10.5897/IJPS11.1469 |
| spellingShingle | Golestaneh, Seyed Jafar Ismail, Napsiah Tang, Sai Hong Mohd Ariffin, Mohd Khairol Anuar Naeini, Hassan Moslemi Maghsoudi, Ali Asghar A committee machine approach to multiple response optimization |
| title | A committee machine approach to multiple response optimization |
| title_full | A committee machine approach to multiple response optimization |
| title_fullStr | A committee machine approach to multiple response optimization |
| title_full_unstemmed | A committee machine approach to multiple response optimization |
| title_short | A committee machine approach to multiple response optimization |
| title_sort | committee machine approach to multiple response optimization |
| url | http://psasir.upm.edu.my/id/eprint/22877/1/22877.pdf |
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