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Title: 運用物料清單理論於試產階段品質之預測
Using Bill of Material theory in the prediction quality of the trial production stage
Authors: 程祖婷
Tsu-Ting Cheng
Contributors: 資訊管理系碩士班
蔡東亦
Keywords: 小樣本學習;虛擬樣本;積層陶瓷電容;物料清單
Small data set;Multi-layer ceramic capacitor;Synthetic sample;Bill of material
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: 被動元件產品的生命週期在全球的競爭壓力之下有越來越短的趨勢,因此如果可以加速新產品研發到量產的這段時間,將有助於提升企業界在市場上的競爭力。在2007年Li等人提出一種方法,適用於在大規模生產之前使用人工類神經網路去進行試產的預測,以獲得更精確的訊息,並將此方法應用在積層陶瓷電容產業的一個案例中。但是,在他們的研究中虛擬樣本的產生卻有很嚴重的錯誤產生。更精確的說,虛擬樣本的產生是靠著一個獨立的因子產生的,但通常因子間屬性都是相互連結的。本研究的目的是基於考慮相關的因子屬性,利用整體趨勢擴散法,來推導出整體模糊樣本來解決此問題。透過跟Li等人使用相同的數據,本研究的結果表示,在小數據中利用屬性間的依賴可以預測使得正確率明顯的比Li等人還來的好,並成功的克服Li等人的缺點。
Under the pressure of global competition, Product life cycles are becoming shorter and shorter, especially in the passive components. The issue of time to market has thus become a core competency for firms to increase market share. Li et al. (2007) proposed a procedure, called mega-trend-diffusion, is applied to predict the dielectric constant of multi-layer ceramic capacitors in powder pilot runs by Artificial Neural Networks. However, in their research, the virtual sample generation process with gross negligence. More specifically, the procedure generates virtual samples one data attribute by one independently. Usually, the attributes are interconnected. The purpose of this research is based on the consideration of dependent data attributes and use Mega-trend-diffusion technique to deduce fuzzy-based synthetic samples to solve this problem. WE using to the same of research data with Li et al. Research results to indicate, in the small sample set can be using among attribute to related of improve the prediction of accuracy rate, and it successfully overcome the disadvantages of Li et al.
Appears in Collections:[資訊管理系(所)] 博碩士論文

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