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研究生:陳志宏
研究生(外文):Chin Hong Chen
論文名稱:適應模糊系統之理論與應用研究
論文名稱(外文):Theory and Applications of Adaptive Fuzzy Systems
指導教授:周志成周志成引用關係
指導教授(外文):Chi Cheng Jou
學位類別:碩士
校院名稱:國立交通大學
系所名稱: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:1993
畢業學年度:81
語文別:英文
論文頁數:118
中文關鍵詞:模糊系統競爭學習教導學習
外文關鍵詞:fuzzy systemcompetitive learningsupervised learning
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本文針對模糊系統發展出含有參數學習和結構調整的競爭學習法則及
教導學習法則,所探討的內容著重在設計的基本原理、運算特性以及模糊
系統本身的適應性。為了實現適應性於模糊系統中,我們建議把模糊系統
中的邏輯法則予以參數化,同時簡化模糊系統的運算模式。根據競爭學習
的基本原理,我們提出參數與結構競爭學習法則使模糊系統能處理分類與
分群問題。依照應用環境的特性,我們列舉數種教導學習法則,使模糊系
統在設計上更加方便。最後,我們發展一個能夠視實際問題需要而自動改
變法則數目的結構學習理論。經由實例模擬證明,我們所提出競爭學習和
教導學習的參數及結構調整方法,對於解決近似函數、樣本分類、向量量
化、資料分群及 系統識別問題皆相當有效。

This thesis presents appropriate model structures for fuzzy
systems, and accompanies these model structures with
parameter-level learning and structure-level learning. The
emphasis of the present exercise is on basic principles of the
design, operating characteristics, and adaptation of fuzzy
systems. In order to incorporate adaptation into fuzzy systems,
a refined mathematical model format for fuzzy systems is
developed in such a way that the fuzzy logical rules in the
systems are parameterized. Two general learning paradigms are
considered: competitive learning and supervised learning.
By incorporating competitive learning into fuzzy system, we
demonstrate that fuzzy systems can be used effectively for
categorization and clustering of unlabeled input patterns.
Methods for dynamically adjusting the parameters and structures
based on fuzzy competitive learning are discussed. To facilitate
system design, we present several supervised learning algorithms
for adjusting parameters. Also, a novel structure-level
supervised learning algorithm that is able to self-organize the
number of fuzzy rules is proposed. The results of simulations
reveal that the proposed parameter-level as well as structure-
level competitive learning and supervised learning algorithms
are practically feasible. Potential applications include
function approximation, pattern classification, vector
quantization, clustering, and system identification.

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