Improving robustness of artificial neural networks model using genetic algorithm
Artificial Neural Networks (ANN) has been widely accepted as process estimators due its ability to capture complex relationships. However, experiences in implementing ANN estimators in research and industry have exposed some weakness that can be detrimental to the overall performance of plant operat...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Universiti Malaysia Sabah
2003
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| Online Access: | http://eprints.utm.my/8025/1/ArshadAhmad2003_ImprovingRobustnessOfArtificialNeuralNetworks.pdf |
| _version_ | 1825910139405729792 |
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| author | Ahmad, Arshad Chen, Wah Sit |
| author_facet | Ahmad, Arshad Chen, Wah Sit |
| author_sort | Ahmad, Arshad |
| collection | ePrints |
| description | Artificial Neural Networks (ANN) has been widely accepted as process estimators due its ability to capture complex relationships. However, experiences in implementing ANN estimators in research and industry have exposed some weakness that can be detrimental to the overall performance of plant operations. Among these, the issue of robustness is of particular importance. This paper proposes adaptation of networks weight as means to improve robustness. Comparisons between GA approach and conventional backpropagation in adaptation of weights are in on-line estimation and control of fatty acid composition in a distillation column. Significant improvements were obtained by the adaptive model especially model generalization perspective. |
| first_indexed | 2024-03-05T18:12:35Z |
| format | Article |
| id | utm.eprints-8025 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T18:12:35Z |
| publishDate | 2003 |
| publisher | Universiti Malaysia Sabah |
| record_format | dspace |
| spelling | utm.eprints-80252010-06-02T01:50:47Z http://eprints.utm.my/8025/ Improving robustness of artificial neural networks model using genetic algorithm Ahmad, Arshad Chen, Wah Sit T Technology (General) Artificial Neural Networks (ANN) has been widely accepted as process estimators due its ability to capture complex relationships. However, experiences in implementing ANN estimators in research and industry have exposed some weakness that can be detrimental to the overall performance of plant operations. Among these, the issue of robustness is of particular importance. This paper proposes adaptation of networks weight as means to improve robustness. Comparisons between GA approach and conventional backpropagation in adaptation of weights are in on-line estimation and control of fatty acid composition in a distillation column. Significant improvements were obtained by the adaptive model especially model generalization perspective. Universiti Malaysia Sabah 2003 Article PeerReviewed application/pdf en http://eprints.utm.my/8025/1/ArshadAhmad2003_ImprovingRobustnessOfArtificialNeuralNetworks.pdf Ahmad, Arshad and Chen, Wah Sit (2003) Improving robustness of artificial neural networks model using genetic algorithm. Proceedings of International Conference On Chemical and Bioprocess Engineering, 2 . pp. 793-800. |
| spellingShingle | T Technology (General) Ahmad, Arshad Chen, Wah Sit Improving robustness of artificial neural networks model using genetic algorithm |
| title | Improving robustness of artificial neural networks model using genetic algorithm |
| title_full | Improving robustness of artificial neural networks model using genetic algorithm |
| title_fullStr | Improving robustness of artificial neural networks model using genetic algorithm |
| title_full_unstemmed | Improving robustness of artificial neural networks model using genetic algorithm |
| title_short | Improving robustness of artificial neural networks model using genetic algorithm |
| title_sort | improving robustness of artificial neural networks model using genetic algorithm |
| topic | T Technology (General) |
| url | http://eprints.utm.my/8025/1/ArshadAhmad2003_ImprovingRobustnessOfArtificialNeuralNetworks.pdf |
| work_keys_str_mv | AT ahmadarshad improvingrobustnessofartificialneuralnetworksmodelusinggeneticalgorithm AT chenwahsit improvingrobustnessofartificialneuralnetworksmodelusinggeneticalgorithm |