Please use this identifier to cite or link to this item: http://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/106892
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dc.contributor黃文吉zh_TW
dc.contributorWen-Jyi Hwangen_US
dc.contributor.author范哲誠zh_TW
dc.contributor.authorZhe-Cheng Fanen_US
dc.date.accessioned2019-09-05T11:42:21Z-
dc.date.available2015-08-06
dc.date.available2019-09-05T11:42:21Z-
dc.date.issued2012
dc.identifierGN0699470137
dc.identifier.urihttp://etds.lib.ntnu.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=id=%22GN0699470137%22.&%22.id.&
dc.identifier.urihttp://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/106892-
dc.description.abstract本論文提出以Recursive Least Mean Square為基礎,結合Fuzzy c-Means分群演算法實作出Radial Basis Function類神經網路之紋理圖辨識系統。在本論文中,Fuzzy c-Means計算紋理圖的質量中心點,Recursive Least Mean Square計算類神經網中的權重係數,希望利用硬體的特性來實現快速運算、低資源消耗、低功率消耗以及擁有良好的效能之硬體架構。 最後我們所提出的硬體架構會在以FPGA為基礎的可程式化系統晶片設計(System On a Programmable Chip,SOPC)之平台上作實際的效能測試。根據使用不同的紋理圖作為測試資料,實驗結果顯示本架構對於紋理圖辨識有良好的分類正確率,且此硬體架構提供了日後高度的延伸性。zh_TW
dc.description.abstractThis paper presents a real time RBF training hardware architecture for texture recognition which is based on recursive least mean square method and fuzzy c-means algorithm. We use fuzzy c-means algorithm to calculate centers in the hidden layer and use recursive least mean square method to estimate connecting weights in the output layer. Experimental results show that the proposed architecture is a effective hardware for real time training with low computational cost, low power consumption and high performance.en_US
dc.description.sponsorship資訊工程學系zh_TW
dc.language中文
dc.subject可程式化系統晶片zh_TW
dc.subject資料分群zh_TW
dc.subjectFCM演算法zh_TW
dc.subjectRecursive Least Mean Squarezh_TW
dc.subject紋理圖辨識zh_TW
dc.subject系統程式晶片設計zh_TW
dc.subjectFPGAen_US
dc.subjectdata clusteringen_US
dc.subjectFCM algorithmen_US
dc.subjectRecursive Least Mean Squareen_US
dc.subjecttexture recognitionen_US
dc.subjectsystem on programmable chipen_US
dc.title基於RBF實現紋理辨識之硬體架構zh_TW
dc.titleRadial Basis Function Hardware Architecture for Texture Classificationen_US
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