An improved approach to automatically build fuzzy model rules
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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 PowerPoint PPT Presentation


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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. 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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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

Outline

  • Motivations

  • The concept of system identification

  • The improved algorithm

  • Simulations and Discussions

  • Conclusions and Future Works


Motivation

Motivation

  • Only I/O data

  • Model construction

  • I/O relation

  • Modification


The concept of system identification

The concept of system identification


Takagi and sugeno s model

TakagiandSugeno’s model


Sugeno and yasukawa s model

SugenoandYasukawa’s model


Fuzzy modeling

Fuzzy modeling


To decide the number of rules

To decide the number of rules


Fuzzy c means clustering

Fuzzy C-means clustering


To determine the number of rules

To determine the number of rules


Coarse fuzzy modeling

Coarse fuzzy modeling

  • Fuzzy C-Regression Model (FCRM)

  • Premise parameters generation

  • Consequent parameters generation


Fuzzy c regression model 1

Fuzzy C-Regression Model (1)


Fuzzy c regression model 2

Fuzzy C-Regression Model (2)


Premise parameters generation 1

Premise parameters generation (1)


Premise parameters generation 2

Premise parameters generation (2)


Premise parameters generation 3

Premise parameters generation (3)


Premise parameters generation 4

Premise parameters generation (4)


Consequent parameters generation

Consequent parameters generation


Fine tuning

Fine tuning


The steepest decent method

The steepest decent method


The gradient of objective function 1

The gradient of objective function (1)


The gradient of objective function 2

The gradient of objective function (2)


The gradient of objective function 3

The gradient of objective function (3)


Stop condition

Stop condition


Example 1 1

Example 1 (1)


Example 1 2

Example 1 (2)

The optimal parameters


Example 1 3

Example 1 (3)


Example 2 1

Example 2 (1)


Example 2 2

Example 2 (2)


Example 3 1

Example 3 (1)


Example 3 2

Example 3 (2)


Conclusions and future works

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


Least squares estimator

Least-squares estimator


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