博碩士論文 etd-0720113-150024 詳細資訊


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姓名 陳錦平(Jin-Ping Chen) 電子郵件信箱 E-mail 資料不公開
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 101學年第2學期
論文名稱(中) 容錯式週期性多數值類神經元
論文名稱(英) Modified Multi-Valued Neuron with Periodic Tolerant Activation Function
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    紙本論文:5 年後公開 (2018-08-20 公開)

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    摘要(中) 多數值類神經元與週期性活化函數(Multi-valued Neuron with Periodic activation function,MVN-P)在這裡也簡稱為週期性多數值類神經元,MVN-P是由Aizenberg所提出用以解決分類問題的一種神經元架構。在MVN-P之中,每兩種不同類別區塊之間的邊界都是固定的,這樣子的因素可能會使得整個神經元在訓練的過程中產生難以收斂或者是沒有辦法收斂的情形。在本篇論文中我們提出了兩個修改MVN-P的模型,而這兩種模型都是基於設計”邊界沒那麼固定”的點子所設計的。我們所提出的第一種模型中,在每兩種不同類別之間的邊界加入了稱為Crisp Buffer的容忍區域,在訓練階段,只要有分類非正確的資料位於此區就可以被容忍其錯誤。在我們所提出的第二種模型中,我們使用了Fuzzy Buffer來做為判斷是否容錯的機制,在訓練階段分類錯誤的資料其歸屬函數(Membership degree)若低於所設定之Threshold,則可以被容忍錯誤。接著我們使用Genetic Algorithm來最佳化上述兩種模型之參數,減少使用者自訂參數的負擔。除此之外,MVN-P難以用以處理資料類別數量較多的分類問題,因此我們發展了一個樹狀架構來克服上述的情況,最後實驗的結果可以得知我們所提出的方法是有效的。
    摘要(英) Multi-valued Neuron with Periodic activation function (MVN-P) was proposed by Aizenberg for solving classification problems. The boundaries between two distinct categories are crisply specified in MVN-P, which may result in slow convergence or being unable to converge at all in the learning process. In this paper, we propose two revised models of MVN-P based on the idea of un-sharp boundaries. In the first revised model, a crisp buffer is provided around a boundary between two distinct categories, allowing incorrect assignments in the buffer to be tolerated in the training phase. In the second revised model, a fuzzy buffer is provided instead and an incorrect assignment with membership degree less than a Threshold can be tolerated. Genetic algorithms are applied to derive optimal values for the parameters involved in different models, alleviating the burden of setting them manually by the user. Besides, MVN-P has difficulties solving the classification problems having a large number of categories. A tree structure is developed to overcome these difficulties. Simulations have been done and the results are presented to demonstrate the effectiveness of our proposed ideas.
    關鍵字(中)
  • 複數數值類神經元
  • 樣本分類
  • 模糊集合
  • 活化函數
  • 基因演算法
  • 樹狀架構
  • 關鍵字(英)
  • fuzzy sets
  • genetic algorithms
  • tree structure
  • Complex-valued neuron
  • pattern classification
  • activation function
  • 論文目次 論文審定書 i
    致謝 iii
    摘要 iv
    Abstract v
    圖次 viii
    表次 x
    第一章 導論 1
    1.1 研究動機與文獻探討 1
    1.2 論文架構 4
    第二章 文獻回顧 5
    2.1 多數值類神經元 5
    2.2 週期性多數值類神經元 9
    第三章 週期性多數值類神經元與容忍區域 14
    3.1 MVN-P with Crisp Buffer 14
    3.2 MVN-P with Fuzzy Buffer 23
    3.3 基因演算法與多數值類神經元之最佳化 27
    3.4 實驗方法 33
    3.5 實驗結果 – l與效能的關係 35
    3.6 實驗結果 – 神經元與GA 39
    第四章 多類別問題 41
    4.1 1-a-a 41
    4.2 樹狀結構 43
    4.3 實驗結果 46
    第五章 多數值類神經元活化函數之變形 48
    5.1 MVN-Sin 49
    5.2 實驗結果 51
    第六章 結論與未來研究方向 53
    參考文獻 54
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    口試委員
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  • 李錫智 - 指導教授
  • 口試日期 2013-07-25 繳交日期 2013-08-20

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