研究生: |
林建旻 |
---|---|
論文名稱: |
以FPGA實現GRNN演算法之特徵分類硬體系統 The Implementation of GRNN-based Feature Classification System Based on FPGA |
指導教授: | 黃文吉 |
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: | 2015 |
畢業學年度: | 103 |
語文別: | 中文 |
論文頁數: | 41 |
中文關鍵詞: | FPGA 、類神經網路 、特徵分類 |
論文種類: | 學術論文 |
相關次數: | 點閱:94 下載:6 |
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本論文針對特徵分類法則實現一套硬體架構,可快速辨認出特徵加以分類。採用的法則是廣義迴歸類神經網路(General Regression Neural Network,GRNN)為監督式學習法則,與其它分類演算法相比,如RBFN (Radial Basis Function Network, RBFN)需要一段時間離線訓練,因此無法適用於即時分類,相較之下GRNN不需要離線訓練且學習速度快,可立刻獲得分類結果。
本論文GRNN法則是以浮點數格式(Floating-point)為基礎實現並驗證現場可程式化邏輯閘(FPGA, Field Programmable Gate Array)上,在一些文獻中於FPGA已實現GRNN法則,但它們電路面積與訓練資料個數成正比,故資源消耗(area cost)量是很可觀的,相較之下本論文設計的電路面積不會與訓練資料的個數成正比,所以在資源消耗方面佔有一定的優勢。此外,文獻中所提出的GRNN法則是以固定點格式(Fixed-point)實現硬體電路,分類的精確度比本電路設計的浮點數格式低,綜合上述所提到的觀點,本電路具有學習速度快、可用於即時分類、資源消耗低,以及分類精確度高的優點。
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