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研究生:唐天生
研究生(外文):Tang Tien-Sheng
論文名稱:自我組織類神經網路之模糊建模
論文名稱(外文):Fuzzy modelling using self-organizing neural networks
指導教授:羅吉昌
指導教授(外文):Lo Ji-Chang
學位類別:碩士
校院名稱:國立中央大學
系所名稱:機械工程研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:1999
畢業學年度:87
語文別:中文
論文頁數:72
中文關鍵詞:自我組織經驗法類神經網路
外文關鍵詞:self-organizing heuristic methodneural network
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本篇論文對於模糊系統(fuzzy system),提出一新的模糊建模(fuzzy modelling)的概念,首先,在模糊規則的後件型態方面,由傳統的單一個後件(consequent)的口語化模糊集合(lingusitic fuzzy set)(IF x is A_i THEN y is B),改變成由經驗法則(heuristic method)求出二個對應的後件口語化模糊集合,以線性組合的方式組合而成(IF x is A_i THEN y is ω_{i1}. B_{i1}+ω_{i2}. B_{i2}),經由訓練出理想的權重ω_{i1} 和 ω_{i2},就可以得到一個理想的模糊規則。其次,為了進一步增加系統的近似能力,我們加入了自我組織經驗學習法(self-organizing heuristic method)來自我增加所需要的模糊規則,達到理想的近似效能。
本篇論文中,我們將所提出的模糊邏輯系統,以類神經網路(neural network)加以詮釋,利用類神經網路已經研究成熟的學習能力,做權重的訓練。因為,只訓練模糊規則後件的權重,所以在類神經網路的權重學習問題上,可減
少計算的時間,與電腦記憶體的需要量,因此收斂的速度就能增快。

A neural-network-based structure learning fuzzy system is proposed. The
consequent of a rule is assumed
to be a linear combination of two fuzzy sets
associated with an output variable as against the traditional
fuzzy rules whose consequents are decided by an experienced operator. The
defuzzified result of these proposed fuzzy rules is proved to conform with the
general meaning of a defuzzifier and is shown to be realizable through a
neural network in which the coefficients associated with the linear
combination are tuned from input-output pairs. To improve its performance
further, we incorporate the proposed system with a self-organizing
heuristic method (SOHM) to generate necessary fuzzy rules
automatically.
Lastly, the capability of this approach is demonstrated through examples.

第一章 緒論
1.1 文獻回顧
1.2 研究動機與目標
1.3 各章節簡介
第二章 模糊邏輯控制理論簡介
2.1 傳統模糊邏輯控制理論
2.2 本篇所提模糊邏輯控制理論
2.3 傳統與本篇所提理論的比較
第三章 自我組織經驗法
3.1 自我組織經驗法的概念
3.2 自我組織經驗法的計算過程
3.3 自我組織經驗法加入我們的模糊系統
第四章 模糊系統與類神經網路的混合結構
4.1 類神經網路簡介
4.2 類神經網路模糊邏輯系統
第五章 逆傳遞學習法則
第六章 系統模擬
6.1 非線性SISO系統
6.2 燃燒爐系統
6.3 Mackey-Glass混沌時序系統
第七章 結論與未來研究方向
7.1 結論
7.2 未來研究方向
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