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研究生:梁耀升
研究生(外文):Liang, Yao-Sheng
論文名稱:應用於可攜式電子鼻資料分類之多類支持向量機晶片
論文名稱(外文):An On-Chip Multi-Class Support Vector Machine Applied to Portable Electronic Nose Data Classification
指導教授:鄭桂忠
指導教授(外文):Tang, Kea-Tiong
口試委員:陳新黃聖傑
口試日期:2011--7-6
學位類別:碩士
校院名稱:國立清華大學
系所名稱:電機工程學系
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:2011
畢業學年度:99
語文別:中文
論文頁數:59
中文關鍵詞:電子鼻支持向量機高斯函數
相關次數:
  • 被引用被引用:0
  • 點閱點閱:333
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  • 下載下載:42
  • 收藏至我的研究室書目清單書目收藏:0
電子鼻近年來被廣泛的應用在各個領域中,在傳統上電子鼻系統仍是一個龐大的裝置,然而若要能方便的在人類的生活中使用,則必須將電子鼻發展成可隨身攜帶的裝置,此外,在某些應用的需求上,例如環境的監控,考量到成本和體積,我們不可能以大型的電子鼻裝置來運作,因此將電子鼻系統微小化是必要的。電子鼻系統的運作從吸入氣味樣本後,使其與氣體感測器反應,接著將反應的訊號轉換和處理,最後透過資料辨識得到結果,是一個結合多個領域的裝置,在氣味資料辨識的部分,其牽涉到圖形辨識的技術,這些分類演算法通常以電腦或微處理器運行,但若要應用在可攜式的裝置上,以此方式並不划算,因此透過低功率的類比積體電路設計實現是較佳的方式。
在圖形辨識的領域中,目前已經發展相當多種的演算法,支持向量機從90年代被提出後,因為根據統計學習理論可以證明支持向量機的原理在分類的許多考量上較佳,因此在許多的領域中蓬勃發展和應用。本研究論文針對可攜式電子鼻提出一個三類支持向量機晶片,以一對一方法將支持向量機由區分兩類別拓展到多類別,並且在同一片晶片上同時實現參數學習和氣味分類的機制,透過一個進一步簡化的遞迴式類神經網路電路實現參數的訓練。晶片以TSMC 0.18μm CMOS製程製作,經由氣體實驗感測器量測的氣體資料測試晶片,從氣味分類實驗的統計結果知其辨識率在七成以上,並且操作在1.8V時消耗的功率僅125μW。實際量測時,最低操作電壓可到1.2V,此時的功耗更只有58μW,因此本研究相當適合應用在可攜式電子鼻上。

摘要 i
ABSTRACT ii
致謝 iii
目錄 iv
圖目錄 vii
表目錄 x
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 章節簡介 4
第二章 文獻回顧 5
2.1 支持向量機介紹 5
2.1.1 支持向量機的發展 5
2.1.2 線性支持向量機 5
2.1.3 線性不可分情形 8
2.1.4 非線性支持向量機 9
2.1.5 常見的核函數 11
2.1.6 多類支持向量機方法 11
2.2 學習方法 15
2.3 相關電路發展 17
第三章 系統架構與模擬 19
3.1 系統規格 19
3.2 二元支持向量機 20
3.2.1 二元支持向量機學習器 20
3.2.2 分類器 22
3.3 區塊電路 23
3.3.1高斯核函數實現 23
3.3.2電流方向選擇電路 26
3.3.3投影算符電路(Projection operator) 27
3.3.4低通濾波器 29
3.3.5積分器 31
3.4 參數範圍 33
3.5 模擬結果 35
3.5.1 氣體資料來源 35
3.5.2 系統模擬結果 37
3.6 電路布局圖 41
第四章 量測結果與討論 42
4.1 晶片量測環境 42
4.1.1 晶片照相圖 42
4.1.2 量測環境 43
4.2 區塊電路量測結果 43
4.2.1 幫浦電路 43
4.2.2 低通濾波器 45
4.3 支持向量機量測結果 46
4.3.1 參數訓練量測 46
4.3.2 氣體資料分類結果 48
4.4 文獻比較表 51
第五章 結論 52
5.1 結論 52
5.2 未來工作 53
參考文獻 54

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