臺灣博碩士論文加值系統

印刷電路板產業為我國每年帶來可觀的經濟貢獻，業者不斷的擴充產能，卻比不上需求的快速變化，造成供給與需求的失衡，普遍存在庫存堆積與部份停工待料的問題。因此，透過一套準確的生產預測模型，可以提供未來的需求趨勢，進而達到後續的物料準備與生產排程等工作，可減少庫存成本與停工的損失。本研究方法共分成四大部份，首先收集總體經濟指標、下游產品需求指標與工業生產指標等共15項因子，利用灰關聯分析篩選出對於PCB需求預測影響性較高的關鍵因子組合；第二部份加入時間序列因子，採用溫氏指數平滑法來考量預測資料的趨勢性與季節性因子的影響性；第三部份以演化式模糊類神經網路為基礎，考量不同的因子權重與採用歐基理德得距離計算模糊規則相似度，以進行模糊規則的分群與規則萃取的訓練階段，並進行加權演化式模糊類神經網路的回想過程；最後，藉由平均絕對百分比誤差(MAPE)、平均絕對偏差(MAD)、誤差均方根差(RMSE)與總成本差(TCD)四項衡量指標，與演化式模糊類神經網路、傳統倒傳遞網路、迴歸分析與基因神經網路進行評比。經實證結果可發現，加權演化式模糊類神經網路皆較其它預測模型效果佳，故本研究所提出之預測模型可作為業者進行需求預測之最佳參考依據。

Printed Circuit Board (PCB) brings a bright and considerable economic contribution to our country every year. Businessmen keep expanding the capacity; however, they cannot keep up with the fast change of demands. Consequently, it causes the imbalance of the supply and demand and it is common to have the problems of severe inventory stacking and material-lacking. Therefore, through an set of accurate production-forecasting model, it can provide a trend of demands in the near future to reach the following material-preparation and production-schedule, etc. It can decrease the stock’s cost and the loss of pending works. The method of this research is divided into four parts in total. First of all, collect overall Economic Index, Lower Product-Demands Index, Industrial Product Indexes and Comparison, and so on. There are fifteen factors in total. Then, use Grey Relation to select out the higher effective key factors’ combination for PCB demand-prediction. In the second part, add time series factor and then use Winter’s Exponential Smoothing to consider the trend of predicted information and the effectiveness of seasonal factors. In the third part, it will proceed the training of WEFuNN, and also work the recall process of the WEFuNN. Finally, via the four measure indexes: MAPE, MAD, RMSE and TCD to compare with the EFuNN, Back-propagation, Regression Analysis,. Through the evidence and result, it shows the WEFuNN gets better effect. As a result, the brought-out predicted model in this research can give the businessmen the best reference to proceed the demand-prediction.

中文摘要-----------------------------------------i
英文摘要-----------------------------------------ii
目錄--------------------------------------------iii
表目錄-------------------------------------------v
圖目錄-------------------------------------------vi
第一章 緒論--------------------------------------1
1.1 研究背景------------------------------1
1.2 研究動機------------------------------2
1.3 研究目的------------------------------3
1.4 研究方法------------------------------3
1.5 研究架構與流程-------------------------4
第二章 文獻探討-----------------------------------6
2.1 預測的定義----------------------------6
2.2 常見的預測方法-------------------------7
2.2.1 傳統的預測方法-------------------7
2.2.2 灰色理論------------------------9
2.3 案例式推理-----------------------------------10
2.3.1 案例式推理的運作流程-------------11
2.3.2 CBR的優點及缺點類神經網路---------11
2.3.3 K最鄰近法---------------------------------12
2.3.3.1 K最鄰近法說明------------12
2.3.3.2 投票法的類別-----------------------------13
2.3.3.3 K最鄰近法的四大步驟-----------------------13
2.4 類神經網路-----------------------------------14
2.5 模糊類神經網路--------------------------------16
2.6 小結 ---------------------------------20
第三章 問題定義與描述------------------------------21
3.1 研究資料來源---------------------------21
3.2 研究變數之說明-------------------------21
3.2.1 時間序列構面---------------------22
3.2.2 總體經濟構面---------------------23
3.2.3 下游產品需求構面-----------------24
3.2.4 工業生產構面---------------------25
3.3 衡量指標--------------------------------------26
第四章 研究方法------------------------------------29
4.1 灰關聯分析-----------------------------31
4.2 溫氏指數平滑法--------------------------33
4.3 資料數值正規化--------------------------36
4.4 加權演化式模糊類神經網路------------------------37
4.4.1 資料前處理階段--------------------37
4.4.2 神經網路訓練階段------------------39
4.4.2 回想過程階段--------------------------------43
第五章 實驗結果與分析-------------------------------47
5.1 灰關聯分析-------------------------------------47
5.2 溫氏指數平滑法----------------------------------48
5.3 加權演化式模糊類神經網路之最佳化參數設計-----------49
5.4 與其它預測模型之效果評比-------------------------54
5.4.1 演化式模糊類神經網路模型-----------------------55
5.4.2 倒傳遞網路模型--------------------------------57
5.4.3 多元迴歸分析預測模型---------------------------58
5.4.4 基因神經網路----------------------------------60
5.5 綜合比較分析------------------------------------62
5.6 研究討論---------------------------------------66
第六章 結論與未來展望--------------------------------68
6.1 結論-------------------------------------------68
6.2 末來展望----------------------------------------68
參考文獻---------------------------------------------70

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