A COMBINED APPROACH TO FUZZY MODEL IDENTIFICATION
A COMBINED APPROACH TO FUZZY MODEL IDENTIFICATION
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DOI：
10.1109/21.293487
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发表时间：
1994-05-01
期刊：
影响因子：
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通讯作者：
SHIH, YP
中科院分区：
文献类型：
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作者：
LEE, YC;HWANG, CY;SHIH, YP
提出了一种离散时间模糊模型识别的组合方法。通过这种方法，识别分两个阶段进行。首先，利用语言方法从采样的非模糊输入输出数据中获得近似模糊关系。然后将该近似模糊关系用作第二阶段的初始估计，其中通过模糊关系方程的数值解析方法确定更准确的模糊关系。推导了一种基于预测误差法的递归辨识算法，通过最小化二次性能指标来最优求解数值模糊关系方程。该算法使得所提出的方法对在线应用程序特别有吸引力。提供了两个数值示例来显示组合方法相对于其他方法的优越性。
A combined approach for discrete-time fuzzy model identification is proposed. By this approach, the identification is performed in two stages. First, the linguistic approach is utilized to obtain an approximate fuzzy relation from the sampled nonfuzzy input-output data. This approximate fuzzy relation is then used as the initial estimate for the second stage in which a more accurate fuzzy relation is determined by the approach of numerical resolution of fuzzy relational equation. A recursive identification algorithm based on the prediction-error method is derived to optimal resolving the numerical fuzzy relational equation by minimizing a quadratic performance index. This algorithm makes the proposed approach particularly attractive to on-line applications. Two numerical examples are provided to show the superiority of the combined approach over other methods.