十倍速時代的來臨，造成企業新產品的研發時程越來越短；也由於客製化生產形態之形成，使得用來預測量產的樣本數量減少。而小樣本問題之所以較難以處理，原因在於樣本與樣本之間存在著顯著的資料間距，同時也由於樣本小以致無法利用統計學的方法推估母體的分配，故以往應用於大樣本的方法常在小樣本的條件下產生相當大的預測誤差。
　　本研究使用整體擴展的技術填補樣本之間所擁有的樣本間距；並且利用母體值域估計技術預測資料行為及產生虛擬樣本；最後以虛擬樣本及其隸屬函數值一同建構一個適用於小樣本的類神經網路。
　　由本研究所舉的三個應用案例的結果可以得知，本研究所提出的研究步驟不僅能適用於模擬的簡化問題中，也能夠應用在實際的案例中，故本研究所提出的方法成功的提高了小樣本資料的學習準確度。

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