臺灣博碩士論文加值系統

本論文根據文獻[6]，以其FCM分群演算法的硬體架構為基礎，實作以非線性高斯核函式為核距離計算之KFCM分群演算法硬體電路，具有管線化以及可以同時計算所有分群之權重係數的能力。此架構改良了以往FCM分群演算法對於非線性資料分群效果不佳的問題，並且能夠應用在帶有雜訊的資料。本論文使用FPGA實現我們提出的硬體架構，並使用Iris data與人工雜訊圖片作為實驗測詴資料。實驗結果顯示本架構對於非線性資料分群效果確實較FCM佳，且架構簡單提供了日後高度的延伸性。

中文摘要 .....................................i
誌謝 .........................................ii
目 錄 ........................................iii
附表目錄 .....................................v
附圖目錄 .....................................vi
第一章 緒論 ..................................1
1.1 研究背景 .................................1
1.2 研究動機與目的 ...........................4
1.3 全文架構 .................................5
第二章 理論基礎與技術背景 ....................7
2.1 Kernel-Based Fuzzy C-Means演算法 .........7
2.2 SOPC 系統整合設計 ........................11
第三章 基礎電路架構介紹 ......................14
3.1 KFCM .....................................14
3.1.1 Pre-computation unit ...................15
3.1.2 Membership Coefficients Updating Unit ..19
3.1.3 Centroid updating unit .................22
3.1.4 Cost function computation unit .........24
第四章 實驗結果與數據探討 ....................26
4.1 開發平台與實驗環境介紹 ...................26
4.2 實驗數據的呈現與討論 .....................29
4.2.1 Iris Data Set分群結果比較 ..............29
4.2.2 加入人工雜訊之圖片分群結果 .............34
第五章 結論 ..................................47
參考著作 .....................................48

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