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研究生:王致乂
研究生(外文):Wang, Chih-I
論文名稱:應用類神經網路於水果氣味分類之研究
論文名稱(外文):Neural Networks for Fruit Odor Classification
指導教授:李建國李建國引用關係
指導教授(外文):Li, Chien-Kuo
口試委員:翁頂升林明華李建國
口試委員(外文):Weng, Ting-ShengLin, Ming-HuaLi, Chien-Kuo
口試日期:2014-06-19
學位類別:碩士
校院名稱:實踐大學
系所名稱:資訊科技與管理學系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:中文
論文頁數:46
中文關鍵詞:電子鼻SOPNN分類正確率類神經網路
外文關鍵詞:e-noseSOPNNClassification AccuracyNeural Network
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電子鼻的應用是近年來被廣為研究的新技術,其應用範圍十分廣泛,例如消防安全的預警措施、協助醫生做醫療診斷、農業產品的成熟檢查與判斷等。系統本身是由軟體與硬體而成,軟體的主要功能在於藉由感知器所量測記錄之資料作為基礎,以便作為分類、辨識、分析的依據,而如何能夠正確的分類與分析感應器的資料便成為電子鼻系統是否成功的一大關鍵,目前有相當多的研究在電子鼻的分類能力上作改善,希望藉由改良的系統可以提升性能。隨著硬體已有顯著的改良,軟體效能的提升成為分類系統成功的重要關鍵,研究學者應用各種不同的方法以期提高分類正確率,其中有很多學者使用類神經網路於此研究,也獲得不錯的結果。
本研究以電子鼻用於水果氣味分類作為例子,使用SOPNN(Sum-of-Product Neural Network)類神經網路於四種水果氣味之分類,四種水果包含香蕉、檸檬、荔枝、龍眼,電腦模擬結果顯示使用SOPNN所得到的分類正確率較其他演算法高。本研究另透過使用不同連續時間點的資料作為輸入變數,來提高分類之正確率,電腦模擬結果顯示使用連續兩個或三個時間點資料作為輸入變數,其所得到的分類正確率較使用單一時間點的資料作為輸入資料時為高。另針對荔枝和龍眼水果氣味不易分類之問題作研究,但電腦模擬結果顯示與其他學者不同的結果,荔枝與龍眼同時存在於分類之水果時,分類的正確率並不會呈現明顯的下降。本研究之結果顯示SOPNN於水果氣味分類問題之可行性,未來可以透過更廣泛的測試,以作為電子鼻建置的參考。

Electronic nose (e-nose) has been studied extensively in recent years. At present, it has a wide range of applications such as fire alarm control system, medical diagnosis, agricultural products inspection and maturity discernment and so on. e-nose system is an integration of both hardware and software, the main function of software is to make decisions, for example classification and detection, based on sensor data. With the advancement of the e-nose hardware technology, the e-nose success relies heavily on the software efficiency. As a result, researchers have applied lots of different algorithms to improve the classification results of e-nose system. Specifically, neural networks have been used in this purpose and good results have been achieved.
In this research, one kind of neural network, Sum-of-Product neural network (SOPNN), was used as a classifier for the fruit odor classification problem in e-nose. Fruit odor of four kinds of fruit, banana, lemon, litchi and longan, were collected by sensors and were used in the simulations. Simulation results showed that SOPNN performed better than other algorithms. And better classification accuracy can be achieved if sensor data of two or three successive time steps is used as SOPNN’s input variables. Moreover, this research also explored the difficult classification problem of litchi and longan due to their similar characteristics. Our simulations showed no prominent decline of classification accuracy when litchi and longan were used in the fruit odor classification problem. This research show the feasibility of applying SOPNN in fruit odor classification problem. The research results can be used as a reference for e-nose system.

第一章 緒論
第一節 研究背景與動機
第二節 研究目的
第三節 研究流程
第二章 文獻探討
第一節 類神經網路學習發展及其架構
第二節 水果氣味分類的研究及其應用
第三節 SOPNN理論應用
第三章 研究方法與實驗架構
第一節 研究架構
第二節 水果氣味測量和分類模擬的實驗環境
第三節 本研究採用之數據
第四節 植入SOPNN演算法於模擬水果氣味的分類
第四章 實驗結果與分析
第一節 四種水果分類的結果
第二節 訓練樣本包含其他時間點的資料
第三節 演算法的比較
第四節 荔枝和龍眼不易分類的問題
第五章 結論與建議
第一節 程式模擬結論
第二節 未來工作與建議
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