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研究生:曾云杉
研究生(外文):Yun-Shan Tseng
論文名稱:整合人工免疫系統為基礎之倒傳遞神經網路與模糊神經網路於銷售量預測
論文名稱(外文):Integration of Artificial Immune System based Back-Propagation Neural Network and Fuzzy Neural Network for Sales Forecasting
指導教授:郭人介郭人介引用關係
指導教授(外文):Ren-Jieh Kuo
口試委員:郭人介
口試日期:2012-05-17
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:工業管理系
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:111
中文關鍵詞:銷售量預測倒傳遞神經網路模糊神經網路人工免疫系統
外文關鍵詞:Sales forecastingBack-propagation networkFuzzy neural networkArtificial Immune System
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由於外在環境因素的不確定性,決策者常需藉由預測模式的輔助,以對商業性運作程序做出最佳決策。在一般預測模式中,由於品牌知名度、促銷等外在變因對產品銷售量產生重大影響,而使以歷史資料為基礎之量化預測方法的準確性失真許多。據此,本研究強調同時考慮量化與質性研究方法對產品銷售量的影響,以提供一個更為準確的預測系統。
為達成此目的,本研究應用模糊神經網路(Fuzzy Neural Network, FNN)和以神經網路為輔之模糊邏輯控制與決策系統(Neural-Network-Based Fuzzy Logic Control and Decision System, NN-FLCS) 來評估影響銷售量之外在變因。此後,將質性外在變因與量化的時間序列銷售量資料整合成倒傳遞神經網路(Back Propagation Network, BPN)的輸入變項,且應用數種以人工免疫系統(Artificial Immune System, AIS)為基礎之演算法訓練BPN,以找到網路內的最適權重組合,使得預測準確度提高,並且各方法亦進行互相比較與分析。
為了評估提出的方法,本研究合作個案廠商提供某特定品牌的筆記型電腦銷售量資料及問卷調查,其研究結果顯示同時考慮量化與質性的資料將可改善預測的結果,此外,經由AIS來找尋BPN的權重組合,可增加預測準確度。
With the uncertainty of the external environment factors, forecasting model has been applied to assist decision makers to make better decision in the business process. In addition, the external factors, such as brand awareness and advertising, could significantly influence the product sales. This makes the quantitative forecasting techniques which are most based on historical sales data, lose their forecasting accuracy. Thus, the emphasis should be made to consider both quantitative and qualitative data simultaneously in order to provide a more accurate sales forecast.
In order to achieve this goal, current study employs fuzzy neural network (FNN), Neural-Network-Based Fuzzy Logic Control and Decision System (NN-FLCS), to evaluate the impact of external qualitative factors on the sales. Thereafter, this qualitative impact and time series sales data are integrated as the inputs for a back-propagation network (BPN). Furthermore, some artificial immune system (AIS)-based algorithms are used to train the back-propagation network in order to find the more appropriate connecting weights for the network with higher forecasting accuracy. The comparison among the artificial immune system-based algorithms is also conducted.
In order to assess the proposed method, a franchise computer company provided the sales data and questionnaire survey for a type of laptop computers. The computational results indicate that considering both qualitative and quantitative data really can improve the forecasting performance. In addition, through using AIS to find the connecting weights for BPN, it can provide more accurate forecasting.
摘要 i
Abstract ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究範圍與限制 2
1.4 研究流程 3
第二章 文獻探討 4
2.1 預測方法 4
2.2 影響銷售量之外在變因 13
2.3 類神經網路與柔性運算法 16
2.3.1 類神經網路(Artificial Neural Network, ANN) 16
2.3.1.1類神經網路架構 17
2.3.1.2類神經網路模式 18
2.3.2 倒傳遞神經網路(Back-Propagation Network, BPN) 20
2.3.2.1 倒傳遞神經網路架構 21
2.3.3 柔性運算於倒傳遞神經網路之應用 26
2.4 模糊理論及其相關應用 28
2.4.1 模糊神經網路 29
2.4.2 模糊邏輯控制與決策系統為基礎的神經網路 30
2.5 人工免疫系統(Artficial Immune System, AIS) 32
2.5.1 人工免疫演算法 34
2.5.2 人工免疫系統之相關應用 39
第三章 研究方法 41
3.1 資料收集 43
3.2 問卷法 43
3.3 模糊神經網路(Fuzzy Neural Network, FNN) 44
3.4 人工免疫系統演算法 47
3.4.1 克隆選擇演算法(CLONALG) 48
3.4.2 最佳化人工免疫網路(opt-aiNet) 51
3.4.3 整合人工免疫系統與最佳化人工免疫網路於倒傳遞神經網路(aiNBSB) 53
3.5 驗證 56
第四章 實驗分析 57
4.1 實驗情境 57
4.1.1 資料介紹 58
4.1.2 資料前處理 59
4.2 田口實驗(Taguchi Method) 61
4.2.1 FNN 61
4.2.2 BP 66
4.2.3 CLONALG_BP 69
4.2.4 aiNBSB 72
4.2.5 演算法學習收斂比較 77
4.3 k重交叉驗證(k-fold cross validation)與比較 81
4.4 檢定 84
第五章 結論與建議 87
5.1 研究結論 87
5.2 研究貢獻 87
5.3 未來研究與建議 88
參考文獻 89
附錄 96
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