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| 研究生： |
范哲誠 Zhe-Cheng Fan |
|---|---|
| 論文名稱： |
基於RBF實現紋理辨識之硬體架構 Radial Basis Function Hardware Architecture for Texture Classification |
| 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： | 99 |
| 語文別： | 中文 |
| 論文頁數： | 63 |
| 中文關鍵詞： | 可程式化系統晶片 、資料分群 、FCM演算法 、Recursive Least Mean Square 、紋理圖辨識 、系統程式晶片設計 |
| 英文關鍵詞： | FPGA, data clustering, FCM algorithm, Recursive Least Mean Square, texture recognition, system on programmable chip |
| 論文種類： | 學術論文 |
| 相關次數： | 點閱：109 下載：7 |
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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。
This 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.
[1] [1] Altera Corporation, NIOS II Processor Reference Handbook, 2011.
[2] S.T. Brassai, L. Bako, Gh. Pana, S. Dan,“Neural control based on RBF network implemented on FPGA,” Proc. IEEE International Conference on Optimization of Electrical and Electronic Equipment, pp.41-46, 2008.
[3] O. Buchtala, A. Hofmann, and B. Sick, “Fast and efficient training of RBF networks,” Lecture Nots in Computer Science, Vol. 2714, pp.43-51, 2003.
[4] B. Cao, L. Chang, and H. Li, “Implementation of the RBF Neural Network on a SOPC for maximum power point tracking,” Proc. IEEE Canadian Conference on Electrical and Computer Engineering, pp. 981-985, 2008.
[5] I.C. Cevikbas, A.S. Ogrenci, G. Dundar, S. Balkir,“VLSI implementation of GRBF (Gaussian Radial Basis Function) networks,” Proc. IEEE International Symposium on Circuits and Systems, Geneva, Switzerland, May 28-31, 2000.
[6] S. Haykin, Neural Networks and Learning machines, 3rd edition, Pearson: New Jersey,2009.
[7] J. Haddadniaa, K. Faeza, M. Ahmadib,“A fuzzy hybrid learning algorithm for radial basis function neural network with application in human face recognition,” Pattern Recognition, Vol. 36, pp.1187-1202, 2003.
[8] G.-H. Lee, S.S. Kim, and S. Jung,“Hardware Implementation of a RBF Neural Network Controller with a DSP 2812 and an FPGA for Controlling Nonlinear Systems,” Proc.International Conference on Smart Manufacturing Application, Korea, 2008.
[9] E. Gatt, J. Micallef and E. Chilton, “Hardware radial basis functions neural networks for phoneme recognition,” Proc. IEEE International Conference on Electronics, Circuits and Systems, Vol. 2, pp.627-630, 2001.
[10] K. Okamoto, S. Ozawa, and S. Abe, “A Fast Incremental Learning Algorithm of RBF Networks with Long-Term Memory,” Proc. IEEE Joint Conference on Neural Networks, Vol. 1, pp.102-107, 2003.
[11] W. Pedrycz,“Conditional fuzzy clustering in the design of radial basis function neural networks,” IEEE Trans. Neural Networks, Vol. 9, pp.601-612, 1998.
[12] H. Sarimveis, A. Alexandridis, and G. Bafas,“A fast training algorithm for RBF networks based on subtractive clustering,” Neurocomputing, Vol. 51, pp.501-505, 2003.
[13] J. K. Sing, S. Thakur, D. K. Basu, M. Nasipuri, and M. Kundu,“High-speed face recognition using self-adaptive radial basis function neural networks,” Neural Computing and Applications, Vol. 18, pp.979-990, 2009.
[14] F. Yang and M. Paindavoine, “Implementation of an RBF Neural Network on Embedded Systems: Real-Time Face Tracking and Identity Verification,” IEEE Trans. Neural Networks, Vol.14, pp.1162-1175, 2003.
[15] Z.-G. Yang and J.-L. Qian,“Hardware Implementation of RBF Neural Network on FPGA Coprocessor,” Communications in Computer and Information Science, Vol. 105, Part I,pp.415-422, 2010.
