面對極小樣本的資料，根據統計理論所做出的決策會傾向於較保守而穩健；然而在生產管理初期，即使可以從有限的資料得到取得知識來做決策，就長期而言，這樣的初期知識勢必會受到未來新進的資料干擾，而變得很不穩定的。更糟的是，瞬息萬變的商品市場迫使決策者必須要及早取得或學習生產管理的知識。我們過去一直將研究主題鎖定在將小樣本的學習機制應用在生產實務上，特別是彈性製造系統的排程問題；目前更進一步針對序列型的小樣本資料，發展出一套漸進學習的步驟，主要的考量還是在於將初期資料所帶來的限制予以放鬆：一、初期所收集的資料量不足；二、資料具有時間性。本研究主要是對於製造初期所取得之小量並具有時間相關性的資料，利用模糊理論的基礎，發展出一個藉由後向式追蹤過程來探索預測未來資料所需的資訊；這其中也包含利用輔助性的樣本產生額外的虛擬資訊。經過實例驗證，這一套機制能加速達成學習任務，並同時進行修正。

Since most statistical theories are not expected to generate significant conclusions
when applied on very small data sets, knowledge derived from limited data gathered
in the early manufacturing stages is considered too fragile for long term production
decisions. Unfortunately, in practice this work has to be done in a competitive
environment. We have discussed learning from small data sets for scheduling flexible
manufacturing systems in previous studies, and this article will focus further on the
developing of an incremental learning procedure for small sequential data sets. The
main consideration concentrates on two properties of data: that the data size is very
small and the data are time-dependent. For a small sequential manufacturing data set
collected in the early stages, this study, on the basis of fuzzy theories and artificial
neural network, developed a unique backward tracking process for exploring
predictive information through the strategy of shadow data generation. The extra
information extracted from the shadow data is proven to accelerate the learning task
and dynamically correct the derived knowledge in a concurrent fashion.

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