Committee neural networks with fuzzy genetic algorithm.
Combining numerous appropriate experts can improve the generalization performance of the group when compared to a single network alone. There are different ways of combining the intelligent systems' outputs in the combiner in the committee neural network, such as simple averaging, gating networ...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Elsevier
2011
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| Online Access: | http://psasir.upm.edu.my/id/eprint/23497/1/Committee%20neural%20networks%20with%20fuzzy%20genetic%20algorithm.pdf |
| _version_ | 1796970371461677056 |
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| author | Jafari , S.A. Mashohor , Syamsiah Varnamkhasti, M. Jalali |
| author_facet | Jafari , S.A. Mashohor , Syamsiah Varnamkhasti, M. Jalali |
| author_sort | Jafari , S.A. |
| collection | UPM |
| description | Combining numerous appropriate experts can improve the generalization performance of the group when compared to a single network alone. There are different ways of combining the intelligent systems' outputs in the combiner in the committee neural network, such as simple averaging, gating network, stacking, support vector machine, and genetic algorithm. Premature convergence is a classical problem in finding optimal solution in genetic algorithms. In this paper, we propose a new technique for choosing the female chromosome during sexual selection to avoid the premature convergence in a genetic algorithm. A bi-linear allocation lifetime approach is used to label the chromosomes based on their fitness value, which will then be used to characterize the diversity of the population. The label of the selected male chromosome and the population diversity of the previous generation are then applied within a set of fuzzy rules to select a suitable female chromosome for recombination. Finally, we use fuzzy genetic algorithm methods for combining the output of experts to predict a reservoir parameter in petroleum industry. The results show that the proposed method (fuzzy genetic algorithm) gives the smallest error and highest correlation coefficient compared to five members and genetic algorithm and produces significant information on the reliability of the permeability predictions. |
| first_indexed | 2024-03-06T07:57:03Z |
| format | Article |
| id | upm.eprints-23497 |
| institution | Universiti Putra Malaysia |
| language | English English |
| last_indexed | 2024-03-06T07:57:03Z |
| publishDate | 2011 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | upm.eprints-234972015-10-06T03:20:57Z http://psasir.upm.edu.my/id/eprint/23497/ Committee neural networks with fuzzy genetic algorithm. Jafari , S.A. Mashohor , Syamsiah Varnamkhasti, M. Jalali Combining numerous appropriate experts can improve the generalization performance of the group when compared to a single network alone. There are different ways of combining the intelligent systems' outputs in the combiner in the committee neural network, such as simple averaging, gating network, stacking, support vector machine, and genetic algorithm. Premature convergence is a classical problem in finding optimal solution in genetic algorithms. In this paper, we propose a new technique for choosing the female chromosome during sexual selection to avoid the premature convergence in a genetic algorithm. A bi-linear allocation lifetime approach is used to label the chromosomes based on their fitness value, which will then be used to characterize the diversity of the population. The label of the selected male chromosome and the population diversity of the previous generation are then applied within a set of fuzzy rules to select a suitable female chromosome for recombination. Finally, we use fuzzy genetic algorithm methods for combining the output of experts to predict a reservoir parameter in petroleum industry. The results show that the proposed method (fuzzy genetic algorithm) gives the smallest error and highest correlation coefficient compared to five members and genetic algorithm and produces significant information on the reliability of the permeability predictions. Elsevier 2011-03 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/23497/1/Committee%20neural%20networks%20with%20fuzzy%20genetic%20algorithm.pdf Jafari , S.A. and Mashohor , Syamsiah and Varnamkhasti, M. Jalali (2011) Committee neural networks with fuzzy genetic algorithm. Journal of Petroleum Science and Engineering, 76 (3-4). pp. 217-233. ISSN 0920-4105 http://www.elsevier.com/ 10.1016/j.petrol.2011.01.006 English |
| spellingShingle | Jafari , S.A. Mashohor , Syamsiah Varnamkhasti, M. Jalali Committee neural networks with fuzzy genetic algorithm. |
| title | Committee neural networks with fuzzy genetic algorithm. |
| title_full | Committee neural networks with fuzzy genetic algorithm. |
| title_fullStr | Committee neural networks with fuzzy genetic algorithm. |
| title_full_unstemmed | Committee neural networks with fuzzy genetic algorithm. |
| title_short | Committee neural networks with fuzzy genetic algorithm. |
| title_sort | committee neural networks with fuzzy genetic algorithm |
| url | http://psasir.upm.edu.my/id/eprint/23497/1/Committee%20neural%20networks%20with%20fuzzy%20genetic%20algorithm.pdf |
| work_keys_str_mv | AT jafarisa committeeneuralnetworkswithfuzzygeneticalgorithm AT mashohorsyamsiah committeeneuralnetworkswithfuzzygeneticalgorithm AT varnamkhastimjalali committeeneuralnetworkswithfuzzygeneticalgorithm |