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譜峰搜索的神經(jīng)網(wǎng)絡(luò)實現(xiàn)

林盛,  殷勤業(yè)

林盛, 殷勤業(yè). 譜峰搜索的神經(jīng)網(wǎng)絡(luò)實現(xiàn)[J]. 電子與信息學報, 1995, 17(1): 1-6.
引用本文: 林盛, 殷勤業(yè). 譜峰搜索的神經(jīng)網(wǎng)絡(luò)實現(xiàn)[J]. 電子與信息學報, 1995, 17(1): 1-6.
Lin Sheng, Yin Qinye. APPROACH FOR APPROXIMATION OF SIGNAL SUBSPACE VIA NEURAL NETWORKS[J]. Journal of Electronics & Information Technology, 1995, 17(1): 1-6.
Citation: Lin Sheng, Yin Qinye. APPROACH FOR APPROXIMATION OF SIGNAL SUBSPACE VIA NEURAL NETWORKS[J]. Journal of Electronics & Information Technology, 1995, 17(1): 1-6.

譜峰搜索的神經(jīng)網(wǎng)絡(luò)實現(xiàn)

APPROACH FOR APPROXIMATION OF SIGNAL SUBSPACE VIA NEURAL NETWORKS

  • 摘要: 本文提出了譜峰搜索的神經(jīng)網(wǎng)絡(luò)方法,作為一個線性規(guī)劃問題,通過構(gòu)造適當?shù)拇鷥r函數(shù)把譜峰搜索與Hopfield線性規(guī)劃網(wǎng)絡(luò)聯(lián)系起來。網(wǎng)絡(luò)中神經(jīng)元的個數(shù)與參數(shù)空間搜索的分辨率無關(guān),而與傳感器陣列中傳感器的個數(shù)成線性關(guān)系,因此神經(jīng)元的個數(shù)并不要求很多,理論分析和仿真實驗證明,該方法實際上與MUSIC方法或RD(實數(shù)域波達方向估計)法是等價的。
    Abstract: A neural method to solve the orthogonality search problem arising in direction-of-arrival (DOA) estimation is proposed. The problem is mapped onto the snergy function of the neural network as a linear programming problem. The most important feature of this method hinges upon the fact that the number of neurons in the network is linearly dependent on the number of sensors in the sensor array, rather than the discretization levels of parameter space. Theoretical analysis and simulation results show that the p...
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  • Schmidt R O. IEEE Trans. on AP, 1986, 34(3):276-280.[2]Kumaresan R, Tufts D W. IEEE Trans. on AES, 1983, AES-19(1):134-139.[3]Stoica P, Nehorai A. IEEE Trans. on ASSP, 1989, ASSP-37(5):720-741.[4]Stoics, P, Nehorai A. MUSIC, Maximum Likelihood anti Cramer-Pao Bound: Further Results and Comparison. IEEE Proc. ASSP, Glasgow: 1989, 2605-2608.[5]Kung S Y, Diamantaras K I. A Neural Network Learning Algorithm for Adaptive Principal[6]Component Extraction (APEX). Proc. ICASSP, Albuquerque, New Mexico: 1990, 861-864.[7]Lin Sheng, Yin Qinye, Adaptive Principal Component Analysts via Neural Networks. IEEE International Conference on Signal Processing, China, 1993, 1224-1227.[8]Jha S, Durrani T. IEEE Trans. on SMC. 1991, SMC-21(5):1192-1201.[9]Lin Sheng, Yin Qinpe. Performance Analysis of Hopfield Neural Networks for DOA Fstimation. IEEE Proc. ISCAS, London: May 1994.[10][9][11]Lin Sheng, Yin Qinye. Bearing Estimation Without Using Complex Transform. IEEE Proe. ISCAS, Chicago: May 1993.[12]Tank D W, Hopfield J J. IEEE Trans. on CAS, 1986, CAS-33(5):533-541.[13]楊宗凱,殷勤業(yè),鄒理和.電路與系統(tǒng)學報,1990,2(2): 97-107.
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  • 文章訪問數(shù):  1841
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  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  1994-02-01
  • 修回日期:  1994-07-12
  • 刊出日期:  1995-01-19

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