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研究生: 歐浩聲
Ou, Hao-Sheng
論文名稱: 以Kernel為基礎之模糊分群演算法硬體架構實現
FPGA Implementation for Kernel-Based Fuzzy C-Means algorithm with Spatial Constraint
指導教授: 黃文吉
Hwang, Wen-Jyi
學位類別: 碩士
Master
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2012
畢業學年度: 100
語文別: 中文
論文頁數: 45
中文關鍵詞: 可程式邏輯陣列KFCM演算法FCM-SC演算法系統程式晶片設計KFCM-SC演算法
英文關鍵詞: FPGA, KFCM algorithm, FCM-SC algorithm, SOPC, KFCM-SC algorithm.
論文種類: 學術論文
相關次數: 點閱:216下載:13
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  • 本論文根據文獻[12]以及文獻[17],以此兩則文獻中提到的FCM-SC分群演算法的硬體架構和KFCM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[12] 再加上空間資訊[17] 後的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KFCM分群演算法對於有雜訊的資料做分群的問題,並且配合KFCM本身可以對非線性資料分群效果較好的能力,所以能夠廣泛地使用在許多的分群資料上,並且都有良好的辨識率。本論文使用FPGA實現我們提出的硬體架構,並使用人工雜訊圖片作為實驗測試資料。實驗結果顯示本架構對於有雜訊的非線性資料分群效果確實較KFCM佳,且架構簡單提供了日後高度的延伸性。

    Based on the FCM-SC (Fuzzy C-Mean with spatial constraint) architecture in reference [12] and the KFCM (Kernel-Based Fuzzy C-Means) architecture in reference [17], KFCM-SC (Kernel-Based Fuzzy C-Means with spatial constraint) hardware architecture is proposed here with non-linear Gaussian kernel function and spatial constraint. Moreover, the KFCM-SC architecture also takes the advantage of the pipeline and it can compute all of the membership coefficients and centers concurrently. Compared to KFCM architecture, KFCM-SC architecture improves the segmentation ability for noisy data by computing the spatial information. With these advantages, it can deal with the non-linear data due to the kernel function, KFCM-SC architecture can be applied to wide of data and it can achieve better segmentation results. KFCM-SC architecture is implemented on FPGA and tested with noisy picture data. The segmentation result shows that KFCM-SC architecture definitely has a better ability with non-linear noisy data compared to KFCM. Because of the simple architecture of the KFCM-SC, it can be extended easily.

    中文摘要 i Abstract ii 誌謝 iii 目 錄 iv 附表目錄 vi 附圖目錄 vii 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機與目的 3 1.3 全文架構 4 第二章 理論基礎與技術背景 5 2.1 Kernel-Based Fuzzy C-Means 演算法 5 2.2 Kernel-Based Fuzzy C-Means with spatial constraint演算法 8 2.3 SOPC系統整合設計 9 第三章 基礎電路架構介紹 12 3.1 Mean Computation Unit 12 3.2 KFCM-SC 分群演算法電路 14 第四章 實驗結果與數據探討 22 4.1 開發平台與實驗環境介紹 22 4.2 實驗數據的呈現與討論 24 第五章 結論 43 參考著作 44

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    [17] 楊斯閔, ”Kernel-Based Fuzzy c-Means分群演算法硬體架構實現”,碩士論文,國立臺灣師範大學資訊工程學系,民國壹佰年

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