| A TWO-PHASE APPROACH TO FUZZY SYSTEM IDENTIFICATION |
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| 作者姓名： | Ta-Wei HUNG Shu-Cherng FANG Henry L.W.NUTTLE |
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| 作者单位： | Department of Information Management Shih Chien University，Taipei，Taiwan 10497，China，Graduate Program in Operations Research and Department of Industrial Engineering North Carolina State University，Raleigh，NC 27695-7906，U.S.A.，Graduate Program in Operations Research and Department of Industrial Engineering North Carolina State University，Raleigh，NC 27695-7906，U.S.A. |
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| 摘 要： | A two-phase approach to fuzzy system identification is proposed. The first phase produces a baseline design to identify a prototype fuzzy system for a target system from a collection of input-output data pairs. It uses two easily implemented clustering techniques: the subtractive clustering method and the fuzzy c-means (FCM) clustering algorithm. The second phase (fine tuning) is executed to adjust the parameters identified in the baseline design. This phase uses the steepest descent and recursive least-squares estimation methods. The proposed approach is validated by applying it to both a function approximation type of problem and a classification type of problem. An analysis of the learning behavior of the proposed approach for the two test problems is conducted for further confirmation.
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| 关 键 词： | 模糊系统 系统辩识 模糊聚类分析 最小二乘估计 |
| 收稿时间： | 25 August 2009 |
A two-phase approach to fuzzy system identification |
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| Ta-Wei HUNG,Shu-Cherng FANG,Henry L.W.NUTTLE.A TWO-PHASE APPROACH TO FUZZY SYSTEM IDENTIFICATION[J].Journal of Systems Science and Systems Engineering,2003,12(4):408-423. |
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| Authors: | Ta-Wei Hung Shu-Cherng Fang Henry L W Nuttle |
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| Institution: | 1. Department ofInformation Management Shih Chien University, Taipei, Taiwan 10497, China
2. Graduate Program in Operations Research and Department of Industrial Engineering North Carolina State University, Raleigh, NC 27695-7906, U.S.A. |
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| Abstract: | A two-phase approach to fuzzy system identification is proposed. The first phase produces a baseline design to identify a prototype fuzzy system for a target system from a collection of input-output data pairs. It uses two easily implemented clustering techniques: the subtractive clustering method and the fuzzy c-means (FCM) clustering algorithm. The second phase (fine tuning) is executed to adjust the parameters identified in the baseline design. This phase uses the steepest descent and recursive least-squares estimation methods. The proposed approach is validated by applying it to both a function approximation type of problem and a classification type of problem. An analysis of the learning behavior of the proposed approach for the two test problems is conducted for further confirmation. |
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| Keywords: | Fuzzy inference systems fuzzy system models fuzzy clustering learning |
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