Hopfield優(yōu)化網(wǎng)絡(luò)用于DOA估計存在的問題
SIMPLE ANALYSIS OF THE NEURAL OPTIMIZATION METHOD FOR DOA ESTIMATION
-
摘要: 本文討論了用Hopfield優(yōu)化網(wǎng)絡(luò)解決DOA估計問題方法的有效性和正確性。雖然這種方法可以避免特征分解和譜峰搜索運算,但在不限制網(wǎng)絡(luò)中輸出為1的神經(jīng)元個數(shù)的條件下,方法中代價函數(shù)的構(gòu)造是不正確的。理論分析和仿真實驗都證實了上述結(jié)論。
-
關(guān)鍵詞:
- 神經(jīng)網(wǎng)絡(luò); 波達方向估計; 譜估計
Abstract: This paper gives a simple analysis of the method of using the Hop-field s optimization neural network to solve the DOA estimation problem. Although the method can avoid the eigendecomposition of data autocorrelation matrix and the orthogonality search of parameter space, theoretical analysis and simulation results show that the construction of the DOA cost function is incorrect on the condition that there is no constraint on the number of outputs of the network. -
Schmidt R O. IEEE Trans. on AP, 1986, Ap-34(3): 276-280.[2]Stoica P, Nehorai A. IEEE Trans. on ASSP, 1989, ASSP-37(5): 720-741.[3]Stoica P, Nehorai A. MUSIC[4]Maximum Likelihood, and Cramer-Rao Bound: Further Results[5]and Comparisons, IEEE Proc. ASSP, 1989, 2605-2608.[6]Kung S Y, Diamantaras K I. A Neural Network Learning Algorithm for Adaptive Principal Component Extraction (APEX), Proc. ICASSP, 1990, Vol. 2, 861-861.[7]Lin Sheng, Yin Qin-Ye. Adaptive Principal Component Analysis Via Neural Networks. IEEE[8]International Conference on Signal Processing, China: 1993, 1224-1227.[9]Jha S, Durrani T. IEEE Trans. on SMC, 1991, SMC-2I(5): 1192-1201.[10]Sharman K C. Maximum Likelihood Parameter Estimation by Simulated Annealing. Proc. ICASSP. 1988, 2741-2744.[11]林盛,殷勤業(yè).信號處理學(xué)報,1994，10(2): 66-74.[12]林盛,殷勤業(yè).電子科學(xué)學(xué)刊,1994,17(1): 1-6. -
計量
- 文章訪問數(shù): 1997
- HTML全文瀏覽量: 102
- PDF下載量: 518
- 被引次數(shù): 0
下載:
