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研究生: 唐凡
Tang, Fan
論文名稱: 語意分類及其應用於兩輪機器人控制
Semantic Classification and Its Application in Two-wheeled Robot Control
指導教授: 呂藝光
Leu, Yih-Guang
學位類別: 碩士
Master
系所名稱: 電機工程學系
Department of Electrical Engineering
論文出版年: 2020
畢業學年度: 108
語文別: 中文
論文頁數: 85
中文關鍵詞: 語意分類卷積神經網路長短期記憶
英文關鍵詞: Semantic classification, CNN, LSTM
DOI URL: http://doi.org/10.6345/NTNU202001402
論文種類: 學術論文
相關次數: 點閱:111下載:11
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  • 本論文目的在建構一語意分類系統,使指令不單局限在單一詞彙或單一描述,例如指令旋轉之後前進及前進之前旋轉視為同樣意思,使機器人不是單純的判斷關鍵詞的順序而是使機器人能夠自行判斷語意後執行動作,讓所接受的指令更為靈活且多樣。語意分類系統建構是先以文字語句作為訓練資料,將詞彙透過詞嵌入的方式轉為數據。接著,使用神經網路進行分類訓練,主要以卷積類神經網路(Convolutional Neural Network, CNN)、長短期記憶(Long Short-Term Memory, LSTM)這兩種神經網路進行建模,CNN 具有優秀的特徵擷取及處理能力,LSTM 則在序列表現異,透過實驗比較這兩種方法,並選擇結果較好的架構應用於兩輪機器人。

    The purpose of this paper is to construct a semantic classification system.The instructions are not limited to a single vocabulary or a single description, and the instructions that the robot can accept are more flexible. The construction of semantic classification system mainly needs to be modeled with two types of neural networks: Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). CNN has excellent feature extraction and processing power, and LSTM has excellent performance in sequence. For comparison, some experiments are performed for two neural networks. Finally, the semantic classification is implemented in a two-wheeled robot.

    謝 辭 i 中文摘要 ii 英文摘要 iii 目 錄 iv 圖 目 錄 vi 表 目 錄 viii 第一章  緒論 1 1.1 研究動機與背景 1 1.2 研究目的 2 1.3 研究方法 2 1.4 論文架構 2 第二章  文獻探討與回顧 4 2.1 語意分類 4 2.2 傳統文本表示方法 5 2.2.1 One-Hot Representation 5 2.2.2 TF-IDF 5 2.3 近代文本表示方法 6 2.4 卷積類神經網路 10 2.5 長短期記憶 15 第三章  語意分類系統建立與兩輪機器人平台 19 3.1 實驗平台 19 3.2 分類模型建立 23 3.2.1 文本資料前處理 23 3.2.2 詞向量 24 3.2.3 CNN模型建構 27 3.2.4 LSTM模型建構 29 3.2.5 損失函數 30 第四章 效能評估與實驗結果 32 4.1 實驗資料 32 4.2 模型評估指標 32 4.3 CNN分類模型實驗 35 4.4 LSTM分類模型實驗 43 4.5 語意分類系統應用於兩輪機器人實驗 49 第五章  結論 80 5.1 結論 80 5.2 未來展望 80 參考文獻 81 自 傳 84 學 術 成 就 85

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