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姓名 方光輝(Guang-hui Fang) 查詢紙本館藏 畢業系所 通訊工程學系 論文名稱 調適性多天線偵測使用精簡型自我組織模糊類神經網路
(Adaptive Multi-antenna Detections Using Compact Self-constructing Fuzzy Neural Networks)相關論文 檔案 [Endnote RIS 格式] [Bibtex 格式] [相關文章] [文章引用] [完整記錄] [館藏目錄] 至系統瀏覽論文 ( 永不開放) 摘要(中) 本篇論文提出的方法為,將精簡型自我組織模糊類神經網(compact self-constructing fuzzy neural network, CSFNN)用於調適性非線性波束形成(beamforming)偵測器上。CSFNN中使用精簡型架構演算法(compact self-constructing learning , CSL),使得CSFNN具有自動擴充類神經網路架構的能力。CSL採用兩項準則,用以限制結構不必要的成長,從而降低複雜度,因此,CSFNN偵測器比起傳統偵測器更能聰明地決定類神經網路架構。而在模擬結果中也顯示出,本篇論文提出的調適性波束形成器比起傳統的線性波束形成器,能在位元錯誤率部分(bit error rate, BER)部分表現更為優越。
摘要(英) A novel adaptive nonlinear beamforming technique is proposed based on a compact self-constructing fuzzy neural network (CSFNN) detector, which is capable of increasing the size of detector structure automatically by a compact self-constructing learning (CSL) algorithm. Moreover, this CSL algorithm adopts two evaluation criteria to limit the unnecessary growth of structure complexity, so the structure of CSFNN detector can be determined more intelligently than that of classical detectors. The simulation results show that the proposed adaptive beamforming approach provides significant performance gains over classical beamforming ones in terms of bit-error rate.
關鍵字(中) ★ 波束形成器
★ 智慧型天線
★ 多天線輸入輸出
★ 類神經關鍵字(英) ★ MIMO
★ beamforming
★ CSFNN
★ neural networks論文目次 摘 要 i
Abstract ii
目 錄 iv
圖 目 錄 vi
表 目 錄 viii
第一章 緒論 (Introduction) 1
1-1 前言 1
1-2 模糊類神經網路 (Fuzzy neural network) 3
1-2.1 類神經網路 3
1-2.2 模糊系統 5
1-2.3 模糊類神經網路 7
1-3 本篇論文組織 10
第二章 智慧型天線 (Smart Antenna) 11
2-1 等距線性天線陣列通道模型(ULA) 13
2-2 波束成型演算法 15
2-2.1 MMSE 17
2-2.2 MBER 20
2-2.3 RBF 24
2-2.4 SRBF 28
第三章 精簡型自我組織模糊類神經網路 (Compact Self-constructing Fuzzy Neural Network) 30
3-1 CSFNN架構 31
3-2 精簡型架構演算法 Compact SL 33
3-3 簡化型倒傳遞演算法 Simplified BP 36
3-4 CSFNN總結 39
第四章 模擬結果 (Simulation results) 43
4-1 系統模擬架構 43
4-2 收斂特性分析 45
4-3 位元錯誤率分析 47
4-4 GBF分析 49
結論 (Conclusion) 51
參考文獻 (Reference) 52
參考文獻 [1] S. Siu, Chia-Lu Ho, and Chien-Min Lee, “TSK-based decision feedback equalizer using an evolutionary algorithm applied to QAM communication systems,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 52, 2005, pp. 596-600.
[2] C. Lee and Y. Lin, “An adaptive neuro-fuzzy filter design via periodic fuzzy neural network,” Signal Processing, vol. 85, 2005, pp. 401-411.
[3] H. Song, C. Wang, Y. He, S. Ma, and J. Zuo, “Decision feedback equalizer based on non-singleton fuzzy regular neural networks,” Journal of Systems Engineering and Electronics, vol. 17, 2006, pp. 896-900.
[4] I.S. Reed, J.D. Mallett, and L.E. Brennan, “Rapid convergence rate in adaptive arrays,” IEEE Transactions on Aerospace Electronic Systems, vol. 10, 1974, pp. 853-863.
[5] M. Ganz, R. Moses, and S. Wilson, “Convergence of the SMI and the diagonally loaded SMI algorithms with weak interference [adaptive array],” Antennas and Propagation, IEEE Transactions on, vol. 38, 1990, pp. 394-399.
[6] S. Chen, N. Ahmad, and L. Hanzo, “Adaptive minimum bit-error rate beamforming,” IEEE Transactions on Wireless Communications, vol. 4, 2005, pp. 341-348.
[7] M.S. Bazaraa, H.D. Sherali, and C.M. Shetty, Nonlinear programming: theory and algorithms, John Wiley and Sons, 2006.
[8] S. Chen, A.K. Samingan, B. Mulgrew, and L. Hanzo, “Adaptive Minimum-BER Linear Multiuser Detection for DS-CDMA Signals in Multipath Channels,” 2000.
[9] S. Chen, K. Labib, and L. Hanzo, “Clustering-Based Symmetric Radial Basis Function Beamforming,” IEEE Signal Processing Letters, vol. 14, 2007, pp. 589-592.
[10] R.C. Lin, W.D. Weng, and C.T. Hsueh, “Design of an SCRFNN-based nonlinear channel equaliser,” IEE Proceedings-Communications, vol. 152, 2005, pp. 771–779.
[11] J. Litva and T.K. Lo, Digital Beamforming in Wireless Communications, Artech House, Inc., 1996.
指導教授 賀嘉律(Chia-Lu Ho) 審核日期 2010-9-28 推文 facebook plurk twitter funp google live udn HD myshare reddit netvibes friend youpush delicious baidu 網路書籤 Google bookmarks del.icio.us hemidemi myshare