臺灣博碩士論文加值系統

在統計資料分析中，必須先已知樣本資料的統計機率分配為何，才能瞭解樣本資料所提供資訊，以利於作出進一步的正確決策分析。傳統上的統計機率分配辨識方法以非參數統計中的適合度檢定為主。然而，適合度檢定亦有其限制，如：樣本個數太少無法作出精確的辨識、分組組數影響辨識結果。有鑑於此，本研究提出應用類神經網路來建構一套統計機率分配之辨識方法，並與傳統統計適合度檢定作比較。此外，本研究舉一數據分析，來說明所提出之應用類神經網路的辨識方法，結果顯示本研究所提出的方法具有高度辨識正確率及時效性。

In Statistic data analysis, we first have to know what kind of statistic probability distribution the data obeys; therefore, we can understand what information the data provides and make a right decision making. In general, a non-parameteric test for goodness of fit is used in distribution recognition. However, there are several constraints and limitation. For example, due to the inadequacy of sample, precise recognition can not be made. In addition, the numbers to divide into groups influence the results. This research presents an effective procedure capable of recognition statistic probability distribution by employing neural network. The proposed approach is compared with non-parameteric test for goodness of fit. A data analysis involving the distribution recognition demonstrates high recognition rate and effectiveness by our proposed approach.

目 錄
摘要 i
Abstract ii
致謝 iii
目錄 iv
表目錄 v
圖目錄 vi
第1章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究範圍 2
1.4 論文架構 3
第2章 研究方法簡介 4
2.1 類神經網路之簡介 4
2.1.1 倒傳遞類神經網路 6
2.1.2 學習向量量化類神經網路 8
2.1.3 類神經網路用於辨識之應用 9
2.2 統計方法之簡介 10
2.2.1 卡方適合度檢定（χ2 test for goodness of fit） 10
2.2.2 Kolmogorov — Smirov 檢定 11
第3章 應用類神經網路辨識統計機率分配 14
第4章 數據分析 18
4.1 釋例說明 18
4.2 辨識模式之建構 18
4.3 模式測試 25
4.3.1 運用統計方法作辨識 25
4.3.2 類神經網路與統計方法之比較 25
第5章 結論 28
5.1 結論 28
5.2 未來展望 28
參考文獻 29
附錄 31

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