博碩士論文 etd-0907109-145621 詳細資訊


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姓名 鄭文豪(Wen-Hau Jeng) 電子郵件信箱 jmonkoffice@gmail.com
畢業系所 電機工程學系研究所(Electrical Engineering)
畢業學位 碩士(Master) 畢業時期 97學年第2學期
論文名稱(中) 自建構式一般化第二型模糊架構於神經模糊系統
論文名稱(英) A Self-Constructing General Type-2 Scheme for Neuro-Fuzzy System Modeling
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    摘要(中) 本論文提供了一個自建構式第二型類神經模糊網路(type-2 fuzzy neural network)的系統模型。要建構一個第二型類神經模糊網路主要包含三個問題:降階(type reduction)、架構鑑別(structure identification)、參數鑑別(parameter identification)。在降階的問題上,使用α平面(α-planes)的概念將第二型模糊集合解構成區間第二型模糊集合(interval type-2 fuzzy set),然後再對區間第二型模糊集合使用KM(Karnik-Mendel)演算法來降階。經過運算,每個α平面會得到一組上下限,將這些平面的上下限計算加權平均即為純量的輸出。這種方式總體計算量非常的大,所以我們提供了一個方法來降低計算量。在降階的基礎上,可以建立出第二型類神經模糊推論系統。對於架構鑑別,使用相似度可增加模糊分群法將資料分群;然後,一群就可以用其特徵化成一條模糊規則(fuzzy rule)。實驗模擬的結果顯示,本論文提供的方法可以在不影響準確度的情況下減少降階所需要的時間。
    摘要(英) We propose a self-constructing general type-2 fuzzy neural network for system modeling. The problems of constructing a general type-2 fuzzy neural network include type reduction, structure identification, and parameter identification. Regarding the type reduction, an α-planes strategy is used to decompose a type-2 fuzzy set into several interval type-2 fuzzy sets, and then apply the Karnik-Mendel algorithm to do type reduction to interval type-2 fuzzy sets. After getting both the lower and upper bound of the output for each α-plane, a crisp output value is obtained by the weighted average method. Since the amount of time required by this method is more demanding, an efficient strategy is proposed to solve this problem. Based on type reduction, a type-2 fuzzy neural network for fuzzy inference can be built. Regarding the structure identification, an incremental similarity-based fuzzy clustering method is used to partition the dataset into several clusters and a local regression model is obtained for each cluster, and then a type-2 fuzzy rule is extracted from each cluster. A hybrid learning algorithm which combines particle swarm optimization and recursive least squares estimator is adopted in the parameter identification to refine the antecedent and consequent parameters, respectively, of fuzzy rules. Simulation results show that our proposed method runs faster in type reduction without deterioration of the forecasting performance and the resulting networks obtained are robust against outliers.
    關鍵字(中)
  • 第二型模糊
  • 關鍵字(英)
  • none
  • 論文目次 摘要 i
    Abstract ii
    圖形與表格目錄 iii
    第一章 導論 1
    1.1 研究動機與文獻回顧 1
    1.2 方法概述 3
    1.3 論文架構 4
    第二章 第二型模糊邏輯 5
    2.1 第二型模糊集合 5
    2.2 第二型模糊邏輯系統 10
    2.3 第二型模糊邏輯系統範例 14
    第三章 加速的降階演算法 19
    第四章 第二型模糊類神經學習演算法 28
    4.1 第二型TSK模糊類神經網路 28
    4.2 架構鑑別與參數鑑別演算法 33
    第五章 範例 37
    第六章 實驗結果 41
    6.1 實驗1 41
    6.2 實驗2 44
    6.3 實驗3 47
    第七章 結論與未來展望 50
    參考文獻 51
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    口試委員
  • 洪宗貝 - 召集委員
  • 林文揚 - 委員
  • 謝朝和 - 委員
  • 郭忠民 - 委員
  • 李錫智 - 指導教授
  • 口試日期 2009-07-22 繳交日期 2009-09-07

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