博碩士論文 etd-0805118-084145 詳細資訊

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姓名 林俞均(Yu-Chun Lin) 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 106學年第2學期
論文名稱(中) 應用自適應神經模糊推理技術預測空氣品質指數
論文名稱(英) Employing Adaptive Neuro-Fuzzy Inference Technique for Prediction of Air Quality Indices
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    紙本論文:5 年後公開 (2023-09-05 公開)

    電子論文:使用者自訂權限:校內 5 年後、校外 5 年後公開

    論文語文/頁數 中文/50
    統計 本論文已被瀏覽 5624 次,被下載 0 次
    摘要(中) 本論文擬提出一個基於模糊類神經網路的空氣品質預測系統,透過歷史的資訊進行訓練,資料以時間序列的型式輸入,便能依照時間的變化來預測空氣品質與環境因子的未來走勢。時間序列預測被廣泛地應用在很多領域,例如金融股市的預測、電力品質的預測等等,其有效性也被證實。由於影響因子的不確定性,我們在預測系統裡加入模糊的元素。我們的預測系統是一個四層的模糊類神經網路,分別是輸入層、模糊層、推理層和輸出層。首先,我們對訓練資料做分群得到模糊群聚,每個群聚的歸屬函數由學習到的平均值和變異值所決定;得到模糊群聚後利用遺傳算法與粒子群優化算法的結合對模糊規則的參數進行優化;系統的輸出是經由模糊推論所導出的。我們提出的方法有下列的優點:(1)加入模糊元素,可以更適當的考慮空氣汙染影響因子的不確定性;(2)模糊群聚契合訓練資料的分布,並且由量化的平均值和變異值所描述;(3)模糊規則是由訓練資料萃取出來的,免除使用者的不便;(4)透過參數優化使模糊規則品質提升;(5) 當新的訓練資料加入時,模糊規則不需要重新產生,有利於系統的更新與維護。
    摘要(英) This paper proposes an air quality prediction system based on neuro-fuzzy neural networks which can be trained through historical record information.
    Time series training data are employed to forecast the air quality and environmental factors in the future. Due to the uncertainty of the involved impact factors, fuzzy elements are added to the forecasting system. Our prediction system is a four-layer fuzzy neural network, consisting of the input layer, fuzzy layer, inference layer, and output layer. First, training data are partitioned into fuzzy clusters whose membership functions are of characterized by the learned means and variances. From these fuzzy clusters. Next, genetic and particle swarm optimization algorithms are applied to refine the parameters of the fuzzy rules. The output of the system, indicating the forecast air quality indices, is derived through the fuzzy inference process.
    Our proposed approach has the following advantages: (1) Adding fuzzy elements can more appropriately deal with the uncertainty of the air pollution impact factors; (2) The distribution of training data can be described more precisely by fuzzy clusters with quantified means and variances; (3) Fuzzy rules are extracted automatically from the training data, instead of being supplied manually by human experts; (4) The obtained fuzzy rules are of high quality, and the associated parameters can be optimized efficiently; (5) When new training data are acquired, the fuzzy rules do not need to be regenerated, which is beneficial to the system update and maintenance.
  • 空氣品質
  • 模糊規則
  • 類神經網路
  • 時間序列預測
  • 參數優化。
  • 關鍵字(英)
  • Air quality
  • Fuzzy rules
  • Neural network
  • Time series
  • Optimization algorithms.
  • 論文目次 論文審定書......i
    第一章 導論......1
       1.1. 研究背景與目的......1
       1.2. 問題描述......2
       1.3. 論文架構......3
    第二章 文獻探討......5
       2.1. 自建構分群(Self-Constructing Clustering, SCC-I)......5
       2.2. 神經模糊系統模型(A Neuro-Fuzzy System Modeling)......6
    第三章 研究方法......7
       3.1. 利用Pearson相關係數計算出變數間的相關性......8
       3.2. 建立訓練樣本......9
       3.3. 透過迭代自建構分群SCC-I進行分群......12
       3.4. 建立Fuzzy rules......14
       3.5. 利用混合行算法優化Fuzzy rules參數......16
       3.6. 預測出空氣品質未來值與計算AQI空氣品質指標......18
       4.1. Pearson相關係數實驗......22
       4.2. PCA測試......23
       4.3. 更新參數的影響......24
       4.4. 與實際政府預測AQI比較......25
       4.5. 台灣台中大里區數據實驗結果......25
       4.6. RMCAB實驗結果......28
       4.6.1. 與ANN-MLP模型比較RMCAB 2015年日平均值......28
       4.6.2. 與ANN-MLP模型比較RMCAB資料比較小時預測結果......31
    第五章 結論與未來展望 34
       4.7. 結論......34
       4.8. 未來研究方向......34
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  • 吳志宏 - 召集委員
  • 侯俊良 - 委員
  • 劉志峰 - 委員
  • 歐陽振森 - 委員
  • 李錫智 - 指導教授
  • 口試日期 2018-07-26 繳交日期 2018-09-05

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