| Improved fuzzy identification method based on Hough transformation and fuzzy clustering |
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| 作者姓名： | 刘福才 路平立 潘江华 裴润 |
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| 基金项目： | This project was supported by the Natural Science Foundation of Heilongjiang province and Doctor Foundation of Yanshan U-niversity. |
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| 摘 要： | This paper presents an approach that is useful for the identification of a fuzzy model in SISO system. The initial values of cluster centers are identified by the Hough transformation, which considers the linearity and continuity of given input-output data, respectively. For the premise parts parameters identification, we use fuzzy-C-means clustering method. The consequent parameters are identified based on recursive least square. This method not only makes approximation more accurate, but also let computation be simpler and the procedure is realized more easily. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation.
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Improved fuzzy identification method based on Hough transformation and fuzzy clustering |
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| Abstract: | This paper presents an approach that is useful for the identification of a fuzzy model in SISO system. The initial values of cluster centers are identified by the Hough transformation, which considers the linearity and continuity of given input-output data, respectively. For the premise parts parameters identification, we use fuzzy-C-means clustering method. The consequent parameters are identified based on recursive least square. This method not only makes approximation more accurate, but also let computation be simpler and the procedure is realized more easily. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation. |
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| Keywords: | fuzzy identification Hough transformation fuzzy clustering recursive least square |
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