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研究生:林瑞杰
研究生(外文):Rui-Jie Lin
論文名稱:使用加成性高斯歸屬函數之模糊類神經網路
論文名稱(外文):Additive Gaussian Membership Functions in Fuzzy Neural Network
指導教授:鄧清政
指導教授(外文):Ching-Cheng Teng
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電機與控制工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:1999
畢業學年度:87
語文別:英文
論文頁數:60
中文關鍵詞:模糊類神經網路高斯函數廣泛近似器歸屬函數模糊類神經網路
外文關鍵詞:FNNgaussian functionuniversal approximatiormembership functionfuzzyneural netwrok
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本論文是以模糊類神經網路(Fuzzy Neural Network)為基礎,提出一個調整歸屬函數的新方法。我們首先介紹模糊類神經網路,此網路具有模糊邏輯及神經網路的特性。第二部份證明高斯函數可以由數個標準差較小的其他高斯函數所組成。第三部份為修改模糊類神經網路的歸屬函數成為五層模糊類神經網路(FNN5)。第四部份利用五層模糊類神經網路去近似幾個函數並證明五層模糊類神經網路是一個廣泛近似器。最後,我們將這個方法應用到調整比例積分(PI)控制器。我們經過模擬後,發覺模糊類神經網路與五層模糊類神經網路在精確度的要求上,都有很良好的模擬結果,但是五層模糊類神經網路在微調時,比模糊類神經網路更具有精確的效果。

In this thesis, a new method to tune the membership functions of fuzzy neural network (FNN) is presented. First we study the FNN it inherits the property of both fuzzy inference system and neural network. Then we present that any gaussian function can be represented by the linear combination of gaussian functions with small standard deviation. Therefore, it can be substituted for the second layer of FNN (called FNN5). We use the FNN5 to approximate some functions and prove that it is a universal approximator. Furthermore, apply this proposed method to tune PI controller based on gain phase margin (GPM) specifications. Both FNN and FNN5 have high performance by the simulation verification, however FNN5 is more accurate than FNN on fine-tuning.

Abstract (Chinese)i
Abstract (English)ii
Acknowledgementsiii
Contentsiv
List of Figuresvi
List of Tablesviii
1 Introduction1
2 Fuzzy Neural Network4
2.1 Fuzzy Inference System and Neural Network4
2.2 Structure of the FNN7
2.3 Basic Nodes Operation9
2.4 Supervised Gradient Descent Learning12
3 Approximations by Using Gaussian Functions16
3.1 Universal Approximation Theorem17
3.2 Examples22
3.3 Composition of the Membership Functions of FNN25
4 Additive Gaussian Membership Function in Fuzzy Neural Network26
4.1 Structure of the FNN527
4.2 Layer Operation of the FNN529
4.3 Supervised Learning32
4.4 Initialization34
4.5 Convergence36
5 Simulation Results39
5.1 Example 1:39
5.2 Example 2:43
5.3 Example 3:47
5.4 Example 4:52
6 Conclusion57
Bibliography58

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