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研究生:王彥文
研究生(外文):Yen-Wen Wang
論文名稱:應用軟性計算於時間序列預測之研究
論文名稱(外文):Study on Time Series Forecasting Problem by Applying Soft-Computing
指導教授:張百棧張百棧引用關係
學位類別:博士
校院名稱:元智大學
系所名稱:工業工程與管理學系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:英文
論文頁數:100
中文關鍵詞:軟性計算模糊規則演化式神經網路銷售預測
外文關鍵詞:Soft-ComputingFuzzy RuleEvolving Neural NetworkSales Forecasting
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在一般的預測問題中,時間序列預測佔了相當重要的一部份,許多時間序列的預測模型也因此而被發展出來,一般來說這些方法可以簡單的區分為統計預測模型以及軟性計算應用預測模型。軟性計算技術已經發展多年,與傳統統計方法不一樣的地方是它可以容忍一些干擾存在於我們所蒐集的資料當中,包含:不精確的資料、具不確定性的資料、不明確的資料等等,因此本研究將利用軟性計算來建構一個混合預測方法,並將該方法應用在預測的問題上。本研究第一部份將利用基因演算法來優化類神經網路的結構,第二部分將利用改良的模糊規則觀念來修正之前的模型,利用相似度的觀念將相似的資料合併,如此,該混合預測方法將能夠節省運算時間、並準確的預測。在最後的實驗結果章節中,我們可以發現該方法的平均誤差僅有2.11%,若是比較其它許多預測模型,都具有相當的優勢,因此我們可以成功的應用該混合預測方法於預測的問題上。
Time series forecasting is one of the important problems in the time series analysis. Many different methods have been developed in this field, and these methods can be distinguished into statistic methods and soft-computing methods roughly. Soft-computing method has been developed for many years. Unlike the traditional statistic methods, it tolerates the interference of the time series data, such as imprecision, uncertainty, partial truth, and approximation. The goal of this research is to develop a hybrid method and successfully applied this method in the forecasting problem. In the first part of this research, we will provide a Soft-computing based hybrid method to improve the structure of neural network. The second part of the research will use the rule-based model to gather the data as rule set on the similar tendency in order to reduce the solving time and increase the accuracy of the forecasting method. The experimental results reveal that the MAPE for our hybrid method is 2.11% which is the best compared to others. In summary, our hybrid method can be applied practically as a sales forecasting tool in the PCB industry.
1 Introduction 1
1.1 Background and Motives 1
1.2 Objectives 2
1.3 Architecture of Research 3
2 Literature Review 4
2.1 Traditional Time Series Forecasting Researches 4
2.2 Advanced Time Series Forecasting Solving Methods 5
2.3 Neural Network Based Forecasting Methods 6
2.4 Genetic Algorithms with Neural Network in Forecasting 11
2.5 Review of Sales Forecasting Researches 14
3 Problem Definition 19
3.1 Background of PCB Sales-forecasting Problem 19
3.2 Characteristics of Variables Selection 20
3.3 Evaluating Performance Index 24
4 Methodology 26
4.1 Data Preprocessing 27
4.1.1 Gray Relation Analysis 27
4.1.2 Winter’s Exponential Smoothing 32
4.2 Back-Propagation Network (BPN) 35
4.3 Evolving Neural Networks (ENN) 39
4.3.1 General Genetic Algorithms Methodologies 39
4.3.2 ENN Construction 46
4.4 Fuzzy Rule Based forecasting Model (FRM) 51
4.4.1 Data Clustering by Self Organization Map (SOM) 52
4.4.2 Generate Fuzzy Rule Based Model (FRM) 56
4.5 Weighted Evolving Fuzzy Neural Networks (WEFuNN) 62
4.5.1 WEFuNN Building 62
5 Experimental Results 71
5.1 Experimental Design 71
5.1.1 Experimental Design for ENN 71
5.1.2 Experimental Design for FRM 75
5.1.3 Experimental Design for WEFuNN 77
5.2 The Result of Winter’s Exponential Smoothing 79
5.3 The Result of BPN Model 80
5.4 The Result of Multiple Regression Analysis Model 82
5.5 The Result of Evolving Neural Network (ENN) 83
5.6 The Result of Fuzzy Rule Model (FRM) 84
5.7 The Result of Weighted Evolving Fuzzy Neural Network(WEFuNN) 87
5.8 Integrated Comparisons 87
6 Conclusion and Future Researches 92
7 References 95
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