博碩士論文 etd-0728104-115337 詳細資訊


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姓名 蔡賢亮(Hsien-Leing Tsai) 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 博士(Ph.D.) 畢業時期 92學年第2學期
論文名稱(中) 監督式類神經網路自動建構演算法及應用
論文名稱(英) Automatic Construction Algorithms for Supervised Neural Networks and Applications
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    摘要(中) 類神經網路已被研究了將近一甲子,在這段時間內雖曾遭遇到瓶頸而研究停滯,但仍有很多好用的類神經網路模型及學習法則先後被提出,而且它們也被廣泛地應用到不同的領域,也獲得不錯的成果,成功地解決了很多傳統演算法難以有效解決的問題。
    然而,當使用者要使用類神經網路來解決問題時,他們都會遇到要使用多大的類神經網路的困擾,也就是說,使用者必需自行決定類神經網路需要幾個隱藏神經層,每個隱藏神經層含有多少隱藏神經元。對於類神經網路的使用者來說,要決定一個合適類神經網路確實是一個相當因難且重要的任務,因為類神經網路的大小會嚴重地影響到它們的效率和品質,唯有適當的類神經網路才能夠有效率地解決問題。
    我們的第一個研究目標就是要提出更好的方法來決定合適的類神經網路。研究的過程中,我們提出一系列的解決方法。我們首先提出應用決策樹來建立類神經網路,我們成功地解決了類神經網路架構的煩惱,同時也改進了學習速度慢的缺陷,但是它只能解two-class的問題,而且它建構出的類神經網路較大。接下來,我們提出使用資訊熵來去除這些缺陷,它可以很容易地建構出multi-class的類神經網路,但它只適合解標準型態的問題。最後,我們再將上個方法延伸至循序性(sequential domain)及結構性(structured domain)的問題,所以我們的方法可以應用的範圍很廣泛。目前,我們正推廣我們的研究至量子資訊世界,正著手研究量子類神經網路(quantum neural networks)自動建構演算法。
    摘要(英) The reseach on neural networks has been done for six decades. In this period, many neural models and learning rules have been proposed. Futhermore, they were popularly and successfully applied to many applications. They successfully solved many problems that traditional algorithms could not solve efficiently .
    However, applying multilayer neural networks to applications, users are confronted with the problem of determining the number of hidden layers and the number of hidden neurons in each hidden layer. It is too difficult for users to determine proper neural network architectures. However, it is very significant, because neural network architectures always influence critically their performance. We may solve problems efficiently, only when we has proper neural network architectures.
    To overcome this difficulty, several approaches have been proposed to generate the architecture of neural networks recently. However, they still have some drawbacks. The goal of our research is to discover better approachs to automatically determine proper neural network architectures. We propose a series of approaches in this thesis. First, we propose an approach based on decision trees. It successfully determines neural network architectures and greatly decreases learning time. However, it can deal only with two-class problems and it generates bigger neural network architectures. Next, we propose an information entropy based approach to overcome the above drawbacks. It can generate easily multi-class neural networks for standard domain problems. Finally, we expand the above method for sequential domain and structured domain problems. Therefore, our approaches can be applied to many applications. Currently, we are trying to work on quantum neural networks.
    We are also interested in ART neural networks. They are also incremental neural models. We apply them to digital signal processing. We propose a character recognition application, a spoken word recognition application, and an image compression application. All of them have good performances.
    關鍵字(中)
  • 動態時間校正法
  • 決策樹
  • 學習法則
  • 資訊熵
  • 模擬退火技術
  • 模糊理論
  • 影像壓縮
  • 類神經網路
  • 關鍵字(英)
  • information entropy
  • image compression
  • neural networks
  • dynamic time warping
  • learning rules
  • decision tree
  • simulated annealing method
  • fuzzy theories
  • 論文目次 摘要 2
    Abstract 3
    第一章 研究動機與簡介
    1.1 類神經網路簡介 10
    1.2 研究動機 11
    1.3 研究目標與成果 14
    1.4 章節介紹 15
    第一部分 Multi-layer Perceptron類神經網路自動建構演算法
    第二章 用決策樹來建構類神經網路
    2.1 決策樹 17
    2.2 萃取邏輯描述 20
    2.3 建構門檻網路 20
    2.3.1 門檻邏輯 21
    2.3.2 門檻值的計算 22
    2.4 建構類神經網路 25
    2.4.1 初始化類神經網路 25
    2.4.2 最終的類神經網路 28
    2.5 實驗 28
    2.6 問題探討與結論 31
    第三章 利用資訊熵建構multi-class類神經網路
    3.1 利用資訊熵量測尋找超平面 34
    3.1.1 隱藏神經元的資訊熵函式 35
    3.1.2 輸出神經元的資訊熵函式 37
    3.1.3 資訊熵量測探討 40
    3.2 差距法則 41
    3.2.1 隱藏神經元的差距法則 41
    3.2.2 輸出神經元的差距法則 44
    3.3 類神經網路建構程序 46
    3.4 實驗結果 47
    3.5 問題探討與結論 51
    第四章 利用資訊熵建構能處理結構化樣本的類神經網路
    4.1 廣義遞迴神經元 54
    4.2 資訊熵量測 57
    4.2.1 隱藏神經元的資訊熵函式 58
    4.2.2 輸出神經元的資訊熵函式 59
    4.3 廣義差距法則 60
    4.3.1 類神經網路架構 61
    4.3.2 隱藏神經元之差距法則 62
    4.3.3 輸出神經元的差距法則 65
    4.3.4 實例說明 67
    4.4 類神經網路建構程序 68
    4.5 改良後的演算法 72
    4.6 實驗結果 74
    4.6.1 實驗一:甲狀腺分類問題 74
    4.6.2 實驗二:英文單字語音辨識 75
    4.6.3 實驗三:相似中文字辨識 76
    4.6.4 實驗四:化學分子結構式分類問題 80
    4.6.5 實驗五:模擬退火演算法 81
    4.6.6 實驗六:含雜訊相似中文字辨識 82
    4.7 問題探討與結論 83
    第二部分 ART- Adaptive Resonance Theory類神經網路自動建構演算法在訊號處理上的應用
    第五章 嵌入樣本融合技術之特徵辨識類神經網路在文字辨識上的應用
    5.1 類神經網路架構 85
    5.2 類神經網路的建構及訓練程序 88
    5.3 類神經網路的辨識程序 91
    5.4 範例 92
    5.5 實驗結果 94
    5.5.1 篩選過學習樣本實驗 94
    5.5.2 未篩選過學習樣本實驗 96
    5.6 問題探討與結論 99
    第六章 ART類神經網路架構在語音辨識上的應用
    6.1 動態時間校正法DTW語音辨識演算法簡介 100
    6.2 我們的方法 101
    6.2.1 類神經網路架構 102
    6.2.2 學習演算法 104
    6.2.3 辨識演算法 106
    6.3 實驗結果 106
    6.4 問題探討與結論 107
    第七章 ART類神經網路架構在影像壓縮上的應用
    7.1 影像壓縮技術簡介 109
    7.1.1 非餘值壓縮法 110
    7.1.2 餘值壓縮法 110
    7.1.3 壓縮效果評估方式 112
    7.2 我們的類神經影像壓縮演算法 112
    7.2.1 類神經網路架構 112
    7.2.2 學習演算法 114
    7.2.3 壓縮演算法 115
    7.2.4 解壓縮演算法 116
    7.3 實驗結果 116
    7.3.1 實驗一:非餘值壓縮法 116
    7.3.2 實驗二:餘值壓縮法 118
    7.3.3 實驗三:利用Lena編碼簿來壓縮其它影像 119
    7.4 問題探討與結論 121
    第八章 結論與未來研究方向 123
    參考文獻 125
    名詞對照表 134
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    口試委員
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