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Please use this identifier to cite or link to this item: http://ir.lib.stu.edu.tw:80/ir/handle/310903100/2689

Title: 運用正弦函數組合技術於試產階段品質之預測
Using sinusoid combination technology in the prediction quality of the pilot production stage
Authors: 林明俊
Ming-Chun Lin
Contributors: 資訊管理系碩士班
蔡東亦
Keywords: 小樣本;極值理論;正弦函數
Small data set;Extreme value theory;Sinusoid combination
Date: 2011
Issue Date: 2011-12-01 13:33:48 (UTC+8)
Publisher: 高雄市:[樹德科技大學資訊管理系碩士班]
Abstract: 小樣本的資料往往使得研究人員難以得到穩定的結果。然而,這樣的問題卻經常發生,例如在預測恐怖攻擊,診斷新的疾病或者是試驗產品的品質預測問題。就本質而言,在這些案例之中皆因為樣本數量不足,所以造成可提供的資訊不足以建立可靠的統計模型,用以提取有用的知識。
故此,本研究對於小樣本問題的處理為應用極值理論估計因子的值域範圍,但由於可獲得資料量不足,故使用虛擬樣本生成的應用技術,用以填補資料間距。此外,並使用實際因子結合正弦函數開發構建虛擬因子,其用意是用來尋找隱藏因子,以建立知識模型。最後使用實際的樣本資料進行實驗,以驗證所提出之方法,並比較類神經網路以及大趨勢擴散法。其結果顯示,小樣本資料若是採用本研究所提出之方法,可以有效降低預測誤差率。
Small data set problems often make it difficult for researchers to obtain robust conclusions. However, such problems often arise, such as in the prediction of a terrorist attacks, new disease diagnoses, and pilot product management knowledge. Essentially, in such cases, either insufficient samples or prediction attributes mean it is statistically hard to build reliable models for extracting useful knowledge.
Faced with a specific small data set problem, this research first applies extreme value theory to estimate the domain range of an attribute, and then uses a virtual sample generation technique to fill the information gaps in the sparse data. Further, the sinusoid combination of real attributes is developed to construct a virtual attribute, which is used to find a more effective attribute to build the knowledge model. A real data set is employed to validate the performance of the proposed method by comparing it with the Artificial Neural Network and Mega-Trend-Diffusion approaches. The results indicate that the prediction error rate can be significantly decreased by applying the proposed method to very small data sets.
Appears in Collections:[資訊管理系(所)] 博碩士論文

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