| 引用本文: | 张平安,李人厚.基于模糊聚类和卡尔曼滤波方法的模糊辨识*[J].控制理论与应用,1996,13(5):639~643.[点击复制] |
| ZHANG Pingan and LI Renhou.Fuzzy identification through Fuzzy Clustering Techniques and Kalman Filter Method[J].Control Theory and Technology,1996,13(5):639~643.[点击复制] |
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| 基于模糊聚类和卡尔曼滤波方法的模糊辨识* |
| Fuzzy identification through Fuzzy Clustering Techniques and Kalman Filter Method |
| 摘要点击 875 全文点击 382 投稿时间：1995-05-15 修订日期：1995-10-10 |
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| DOI编号 |
| 1996,13(5):639-643 |
| 中文关键词 模糊辨识 模糊聚类 卡尔曼滤波 系统辨识 |
| 英文关键词 fuzzy identification fuzzy clustering Kalman filter,system identification |
| 基金项目 |
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| 中文摘要 |
| 本文提出一种通用的基于模糊聚类和卡尔曼滤波方法的模糊辨识方法．模糊聚类方法在给定的广义目标下按线性簇对被辨识的样本数据进行聚类，这样使得被辨识模型可用若于局部线性模型表示，然后，利用卡尔曼滤波方法拟合这些线性模型．本文给出了详细的模糊辨识算法．为了验证该辨识方法的有效性，本文最后给出了熟知的Box－Jenkins数据的辨识结果． |
| 英文摘要 |
| This paper discusses a general approach to fuzzy identification based on the fuzzy clustering techniques and Kalman filter method. The fuzzy clustering method utilizes a generalized objective function involving a collection of linear varieties. In this way the identified model is distributed and consists of a series of 'local' linear-type model，then the Kalman filter can be used to fit them as accurately as possible. A detailed identification algorithm is given in this paper. To clarity the advantages of the proposed method，it is used to identify the well-known Box--Jenkins data set，and the result is shown at the end of this paper. |