Fuzzy Pruning Based LS-SVM Modeling Development for a Fermentation Process
Due to the complexity and uncertainty of microbial fermentation processes, data coming from the plants often contain some outliers. However, these data may be treated as the normal support vectors, which always deteriorate the performance of soft sensor modeling. Since the outliers also contaminate...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/794368 |
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| _version_ | 1832553619823525888 |
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| author | Weili Xiong Wei Zhang Dengfeng Liu Baoguo Xu |
| author_facet | Weili Xiong Wei Zhang Dengfeng Liu Baoguo Xu |
| author_sort | Weili Xiong |
| collection | DOAJ |
| description | Due to the complexity and uncertainty of microbial fermentation processes, data coming from the plants often contain some outliers. However, these data may be treated as the normal support vectors, which always deteriorate the performance of soft sensor modeling. Since the outliers also contaminate the correlation structure of the least square support vector machine (LS-SVM), the fuzzy pruning method is provided to deal with the problem. Furthermore, by assigning different fuzzy membership scores to data samples, the sensitivity of the model to the outliers can be reduced greatly. The effectiveness and efficiency of the proposed approach are demonstrated through two numerical examples as well as a simulator case of penicillin fermentation process. |
| format | Article |
| id | doaj-art-9b91a921b67c490eb6ce634e5d959dc4 |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-9b91a921b67c490eb6ce634e5d959dc42025-02-03T05:53:41ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/794368794368Fuzzy Pruning Based LS-SVM Modeling Development for a Fermentation ProcessWeili Xiong0Wei Zhang1Dengfeng Liu2Baoguo Xu3Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, ChinaSchool of Internet of Things Engineering, Jiangnan University, Wuxi 214122, ChinaSchool of Internet of Things Engineering, Jiangnan University, Wuxi 214122, ChinaSchool of Internet of Things Engineering, Jiangnan University, Wuxi 214122, ChinaDue to the complexity and uncertainty of microbial fermentation processes, data coming from the plants often contain some outliers. However, these data may be treated as the normal support vectors, which always deteriorate the performance of soft sensor modeling. Since the outliers also contaminate the correlation structure of the least square support vector machine (LS-SVM), the fuzzy pruning method is provided to deal with the problem. Furthermore, by assigning different fuzzy membership scores to data samples, the sensitivity of the model to the outliers can be reduced greatly. The effectiveness and efficiency of the proposed approach are demonstrated through two numerical examples as well as a simulator case of penicillin fermentation process.http://dx.doi.org/10.1155/2014/794368 |
| spellingShingle | Weili Xiong Wei Zhang Dengfeng Liu Baoguo Xu Fuzzy Pruning Based LS-SVM Modeling Development for a Fermentation Process Abstract and Applied Analysis |
| title | Fuzzy Pruning Based LS-SVM Modeling Development for a Fermentation Process |
| title_full | Fuzzy Pruning Based LS-SVM Modeling Development for a Fermentation Process |
| title_fullStr | Fuzzy Pruning Based LS-SVM Modeling Development for a Fermentation Process |
| title_full_unstemmed | Fuzzy Pruning Based LS-SVM Modeling Development for a Fermentation Process |
| title_short | Fuzzy Pruning Based LS-SVM Modeling Development for a Fermentation Process |
| title_sort | fuzzy pruning based ls svm modeling development for a fermentation process |
| url | http://dx.doi.org/10.1155/2014/794368 |
| work_keys_str_mv | AT weilixiong fuzzypruningbasedlssvmmodelingdevelopmentforafermentationprocess AT weizhang fuzzypruningbasedlssvmmodelingdevelopmentforafermentationprocess AT dengfengliu fuzzypruningbasedlssvmmodelingdevelopmentforafermentationprocess AT baoguoxu fuzzypruningbasedlssvmmodelingdevelopmentforafermentationprocess |