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模糊模型規則庫自動建立之演算法 An improved approach to automatically build fuzzy model rules

模糊模型規則庫自動建立之演算法 An improved approach to automatically build fuzzy model rules. 王乃堅 ( Nai-Jian Wang) 台灣科技大學電機系 中華民國九十年十月二十日 地點：政大經濟系. Outline. Motivations The concept of system identification The improved algorithm Simulations and Discussions Conclusions and Future Works. Motivation.

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模糊模型規則庫自動建立之演算法 An improved approach to automatically build fuzzy model rules

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  1. 模糊模型規則庫自動建立之演算法An improved approach to automatically build fuzzy model rules 王乃堅 (Nai-Jian Wang) 台灣科技大學電機系 中華民國九十年十月二十日 地點：政大經濟系

  2. Outline • Motivations • The concept of system identification • The improved algorithm • Simulations and Discussions • Conclusions and Future Works

  3. Motivation • Only I/O data • Model construction • I/O relation • Modification

  4. The concept of system identification

  5. TakagiandSugeno’s model

  6. SugenoandYasukawa’s model

  7. Fuzzy modeling

  8. To decide the number of rules

  9. Fuzzy C-means clustering

  10. To determine the number of rules

  11. Coarse fuzzy modeling • Fuzzy C-Regression Model (FCRM) • Premise parameters generation • Consequent parameters generation

  12. Fuzzy C-Regression Model (1)

  13. Fuzzy C-Regression Model (2)

  14. Premise parameters generation (1)

  15. Premise parameters generation (2)

  16. Premise parameters generation (3)

  17. Premise parameters generation (4)

  18. Consequent parameters generation

  19. Fine tuning

  20. The steepest decent method

  21. The gradient of objective function (1)

  22. The gradient of objective function (2)

  23. The gradient of objective function (3)

  24. Stop condition

  25. Example 1 (1)

  26. Example 1 (2) The optimal parameters

  27. Example 1 (3)

  28. Example 2 (1)

  29. Example 2 (2)

  30. Example 3 (1)

  31. Example 3 (2)

  32. Conclusions and Future Works • 架構精簡，彈性大 • 易於在電腦上實現 • 不錯的運算效率和較佳的近似結果 • 有較佳的能力去描述未知系統 • 改進FCM方法不足之處 • 以其他的最佳化方法取代最陡坡降法

  33. Least-squares estimator

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