近年來人工智慧的手法逐漸的受到重視,其中模糊倒傳遞網路因有效融合模糊理論可善加利用人類處理事物的經驗法則以及類神經網路高度記憶能力、容錯能力並藉由數據資料集合進行自我學習的優點,被廣泛且有效的應用在銷售預測問題上。本研究以下游需求、總體經濟和工業生產指數三個不同構面所形成的15項變數,利用逐步迴歸和模糊德爾菲法兩種工具,篩選出對於銷售預測影響較高的變數組合,同時以專家問卷形式為各變數進行模糊化工作,代入模糊倒傳遞網路以進行資料測試和學習工作,最後藉由預測涵蓋檢定、預測誤差和準確度三項指標將模糊倒傳遞網路之實驗結果與灰色預測、多元迴歸分析和傳統倒傳遞網路進行評比。經實驗結果可發現,模糊倒傳遞網路之模型可所包含對於預測目標的資訊涵蓋於灰色預測和多元迴歸分析,進一步以預測誤差和準確度來進行評比,則本研究所提出之模糊倒傳遞網路MAPE為3.09%、MAR為97.61%,在此兩項的評比中,都優於其他三種不同的預測模型。
Reliable prediction of sales can improve the quality of business strategy. In this research, fuzzy logic and artificial neural network are integrated into the fuzzy back-propagation network (FBPN) for printed circuit board industry. The fuzzy back propagation network is constructed to incorporate production-control expert judgments in enhancing the performance. Parameters chosen as inputs to the FBPN are no longer considered as of equal importance, but some production control experts are requested to express their opinions about the importance of each input parameter in predicting the sales with linguistic terms, which can be converted into pre-specified fuzzy numbers, aggregated and corresponding input parameter when fed into the FBPN. Subsequently, the arithmetic for triangular fuzzy numbers is applied to deal with all calculation involved in network learning. The proposed system is evaluated through the real life date provide by GCE company. Model evaluation results for research indicate that the Fuzzy back-propagation outperforms the other three different forecasting models in both MAPE and MAR.