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研究生:王慶豐
研究生(外文):Ching-Feng Wang
論文名稱:複數模糊集成學習方法於趨勢預測之研究
論文名稱(外文):Complex Fuzzy Ensemble Learning Method for Trend Prediction
指導教授:李俊賢李俊賢引用關係
指導教授(外文):Chunshein Li
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊管理學系
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:2019
畢業學年度:107
語文別:中文
論文頁數:65
中文關鍵詞:複數模糊類神經複數模糊集特徵選取粒子群最佳化演算法遞迴最小平方法集成學習
外文關鍵詞:complex neuro-fuzzycomplex fuzzy setfeature selectionparticle swarm optimizationrecursive least squares estimatorensemble model
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面對現今的大數據時代,資料的價值需要由資訊技術不斷創造,甚至進一步地預測資料的發展趨勢,人工智慧中的深度學習即為當今預測的最佳工具之一。本研究提出一種新形態之複數模糊類神經分類模型 (Complex Neuro-Fuzzy Classification Model, CNFC),透過複數高斯模糊集合的特性,模糊化輸入資料的類別隸屬程度,更加精確描述類別值域,增強模型的預測及應用能力。以減法分群演算法 (Subtractive Clustering Algorithm, SCA) 識別資料趨向類別,輔助模型進行動態式分類預測,其中採用粒子群最佳化演算法 (Particle Swarm Optimization, PSO) 與遞迴最小平方法 (Recursive Least Squares Estimator, RLSE) 為複合式最佳化演算法 (Hybrid optimization algorithm),針對模型不同部分的參數進行優化,將有效提升模型優化效率。實驗透過重複性與集成學習方法進行多樣化的文獻模型效能比較,驗證CNFC的預測效能與PSO-RLSE的最佳化成效於股價時間序列資料具有較佳能力。
Facing the current era of big data, the value of information is revealed constantly by information technologies, and even further predict the future trend of the data, deep learning in artificial intelligence is one of the best tools for current prediction. This study proposes a novel Complex Neuro-Fuzzy Classification Model (CNFC), through the characteristics of complex Gaussian fuzzy sets, the class degree of input data is fuzzified, which more accurately describes the class value and enhances the prediction and application ability of the model. Identify the directional classification of data by Subtractive Clustering Algorithm (SCA) and assisting models for dynamic classification prediction, the model uses Particle Swarm Optimization (PSO) and Recursive Least Squares Estimator (RLSE) as the hybrid optimization algorithm for parameters optimization of different parts of the model will effectively improve the efficiency of model optimization. The experiment verifies the predictive performance of CNFC and the optimization effect of PSO-RLSE have better ability in stock price time series data through repetitiveness and ensemble learning methods for a variety of literature model performance comparisons.
中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
符號說明 viii
一、 緒論 1
1-1 研究背景 1
1-2 研究動機與目的 1
1-3 研究方法概述 3
1-4 論文架構 3
二、 文獻探討 5
2-1 分群演算法 5
2-2 特徵選取 5
2-3 模糊集合 6
2-4 機器學習最佳化演算法 7
三、 研究方法 9
3-1 特徵選取 9
3-2 複數模糊集合 15
3-3 複數模糊類神經分類模型 17
3-4 PSO-RLSE複合式最佳化演算法 21
四、 實驗 27
4-1 實驗一:日經平均指數時間序列分類預測 28
4-2 實驗二:韓國綜合股價指數時間序列分類預測 32
4-3 實驗三:美國標準普爾500指數時間序列分類預測 36
五、 討論 41
六、 結論與未來研究方向 44
6-1 結論 44
6-2 未來研究方向 44
參考文獻 46
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