複數模糊類神經網路在多目標財經預測

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姓名

林哲渝(Zheyu Lin) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

複數模糊類神經網路在多目標財經預測
(Complex Neural Fuzzy System for Multi-Objective Finance Prediction)

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摘要(中)

本研究使用複數的模糊集合(Complex fuzzy set)取代模糊類神經網路(Fuzzy neural network)中的模糊集合，在演算法上以粒子群最佳化演算法(Particle Swarm Optimization, PSO)為基礎提出了改良的多群粒子群演算法(Multi-group particle Swarm Optimization, PSO)，並且結合遞迴最小平方估計法(Recursive least squares estimator, RLSE)成一個複合式的演算法，此外在模型輸入的選擇上基於夏農資訊熵(Shannon Entropy)提出了特徵選取方法，在本篇論文中將運用特徵選取、複數模糊類神經網路以及混合式的機器學習演算法於股票以及匯率上時間序列的預測。本篇論文使用的特徵選取方法是透過計算特徵對於目標所提供的資訊量，並以一套選取策略針對特徵提供的資訊量多寡進行特徵的挑選。複數模糊集合比傳統一般的模糊集合具有更佳的解釋能力，運用於模糊類神經網路中能夠傳遞更大量的資訊，增加模型預測的效能，並且能夠讓模型能夠同時預測多達6個目標。在模型學習階段，改良式的多群粒子群最佳化演算法能夠比原本的粒子群最佳化演算法更快速的收斂，並且增加找到全局最小化的機率，另外與遞迴最小平方估計法結合能夠減少多群粒子群最佳化演算法需要學習的參數數量，增加多群粒子群最佳化演算法的效能，並且使用遞迴最小平方估計法以計算的方式找出最佳的近似解，而不是透過長時間的訓練，能夠減少模型整體的訓練時間。本篇研究使用股票以及匯率做為多目標的實驗，從實驗結果顯示本論文所使用的特徵選取、模型以及機器學習演算法都有良好的結果。

摘要(英)

In this study, complex fuzzy sets are used to replace fuzzy sets in neural fuzzy systems. Based on the particle swarm optimization(PSO) algorithm, an improved multi-group particle swarm optimization(MGPSO) algorithm is proposed, and combined with the well-known recursive least squares estimation (RLSE) into a hybrid algorithm, called the MGPSO-RLSE learning method. In addition, a feature selection method based on Shannon entropy is presented to select useful information by significant features which will be used as model inputs in modeling. In this study, the feature selection, complex neural fuzzy system (CNFS) with Takagi-Sugeno (T-S) If-Then rules and hybrid machine learning algorithm are used for finance time series prediction of stock price and exchange rate. For the CNFS, the parameters of If-parts are evolved by the MGPSO while the parameters of Then-parts are updated by the RLSE. Complex fuzzy sets have better ability to interpret the set-element membership description than regular real-valued fuzzy sets. They can be used in neural fuzzy networks to transmit more information, increasing the prediction performance of model. Moreover, due to CFSs used in the proposed CNFS, the model is capable to deal with up to six targets simultaneously. In the model learning stage, the MGPSO, compared to one single swarm of PSO, can increase the probability of finding the optimal solution, with fast learning convergence. In addition, the combination of the RLSE to the MGPSO can lessen the burden of machine learning by the MGPSO alone. Several real-world data sets of stock prices and exchange rates have been used to test the proposed approach in the experiments for multi-objective prediction. Through the experimental results, the proposed approach has shown good performance.

關鍵字(中)

★ 複數模糊集合
★ 複數模糊類神經網路
★ 粒子群最佳化演算法
★ 多群粒子群最佳化演算法
★ 遞迴最小平方估計法
★ 特徵選取
★ 夏農資訊熵
★ 股票時間序列
★ 匯率時間序列

關鍵字(英)

★ Complex fuzzy set
★ Complex fuzzy neural system
★ Multi-group particle swarm optimization algorithm
★ Recursive least squares estimation
★ Feature selection
★ Shannon entropy
★ Time series prediction

論文目次

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 1
第一章緒論 2
1.1 研究背景與目的 2
1.2 研究方法 3
1.3 論文架構 4
第二章文獻探討 5
2.1 特徵選取 5
2.2 模糊集合理論與模糊規則 6
2.3 複數模糊集合理論 10
2.4 模糊類神經網路 11
2.5機器學習演算法 12
2.5.1 粒子群最佳化演算法 13
2.5.2 遞迴最小平方估計法 14
第三章系統設計與架構 16
3.1 特徵選取 16
3.2 多目標複數模糊類神經模型計算 20
3.3 複合式機器學習演算法 24
3.3.1 多群粒子群最佳化演算法 24
3.3.2 遞迴最小平方估計法 25
3.4 研究整體流程 26
第四章實驗實作與結果 27
4.1 實驗1:單目標股票指數預測-TAIEX(1998) 27
4.2 實驗2:雙目標股票指數預測-TAIEX(2000)、DJIA(2000) 32
4.3 實驗3: 四個目標匯率預測- GBP/USD、CAD/USD、CHF/USD、DEM/USD 38
第五章實驗結果討論 48
第六章結論 50
6.1結論 50
6.2未來研究方向 51
參考文獻 53
附錄 57
實驗一-學習後參數 57
實驗二-學習後參數 58
實驗三-學習後參數 59

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指導教授

李俊賢(Chunshien Li)

審核日期

2017-8-22

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