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基于徑向基函數(shù)網(wǎng)絡的雷達目標一維像識別技術研究

黃德雙,  保錚

黃德雙, 保錚. 基于徑向基函數(shù)網(wǎng)絡的雷達目標一維像識別技術研究[J]. 電子與信息學報, 1995, 17(1): 26-34.
引用本文: 黃德雙, 保錚. 基于徑向基函數(shù)網(wǎng)絡的雷達目標一維像識別技術研究[J]. 電子與信息學報, 1995, 17(1): 26-34.
Huang Deshuang, Bao Zheng. THE STUDY ON RECOGNITION TECHNIQUE OF RADAR TARGET S ONE DIMENSIONAL IMAGE BASED ON RADIAL BASIS FUNCTION NETWORK[J]. Journal of Electronics & Information Technology, 1995, 17(1): 26-34.
Citation: Huang Deshuang, Bao Zheng. THE STUDY ON RECOGNITION TECHNIQUE OF RADAR TARGET S ONE DIMENSIONAL IMAGE BASED ON RADIAL BASIS FUNCTION NETWORK[J]. Journal of Electronics & Information Technology, 1995, 17(1): 26-34.

基于徑向基函數(shù)網(wǎng)絡的雷達目標一維像識別技術研究

THE STUDY ON RECOGNITION TECHNIQUE OF RADAR TARGET S ONE DIMENSIONAL IMAGE BASED ON RADIAL BASIS FUNCTION NETWORK

  • 摘要: 本文研究了利用遞推最小二乘(RLS)方法訓練的徑向基函數(shù)網(wǎng)絡(RBFN)用于雷達目標一維像的識別與分類問題。證明了RBFN與Parzen窗函數(shù)概率密度估計的等價性,指出RBFN隱層單元傳輸函數(shù)可以推廣到一般的Psrzen概率核函數(shù)或勢函數(shù)形式。還就高斯、三角、雙指數(shù)三種核函數(shù)討論了徑向基函數(shù)網(wǎng)絡形狀參數(shù)、遞推最小二乘算法的遺忘因子對識別結果的影響以及與網(wǎng)絡訓練時間的關系。
    Abstract: This paper studies the problem applying Radial Basis Function Network (RBFN) which is trained by the traditional Recursive Least Square Algorithm (RLSA) to the recognition of one dimensional image of radar targets. The equivalence between RBFN and the estimation of Parzen window probabilistic density is proved, it is pointed out that the I/O functions in RBFN hidden units can be extended to general Parzen window probabilistic kernel function or potential function, too. This paper discusses the effects of the shape parameter in RBFN and the forgotten factor , in RLSA on the results of the recognition of three kinds of kernel function such as Gaussian, Triangle, Double-exponential kernel functions, at the same time, and discusses also the relationship between and the training time in RBFN.
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  • Rumelhaart D B, Hintonn G B, Williams R J. Nature, 1986, 32(3): 533-536.[2][2]Vogl T P. Biological C ybernics, 1988, 59(4): 257-263.[3]Dahl B D. Accelerated Learning the Generalized Delta Rule. IJCNN, Vol.Ⅱ,Washington DCt 1989, 523-530.[4][4 Hush D R. Salas J M. Improving the Learning Rate of Back-Propagation Wigradient Reuse Algorithm, lJCNN, Vol.Ⅰ, San Diego: 1988, 441-447.[5]Parker D B. Optimal algorithms for adaptive networks, Second order back propagation, second order direct propagation, and second order Hebbian learning, IJCNN, Vo1.Ⅱ, San Diego: Nov. 1987, 593-600.[6]Sadjadi M R A, Lion R J. IEEE Trans. on SP, 1992, SP-40(2): 446-450.[7]Chan L W, Fallside F. Computer Speech and Language, 1987, 21(2): 234-240.[8]Kollias S, Anastassiou D. Adaptive Training of Multilayer Neural Networks Using a Least Squares Estimation Technique. HCNN, Vol.1, Washington DC: 1989, 383-390.[9]Kollias S, Anastassiou D. IEEE Trans. on CAS, 1989, CAS-36(8): 1092-1101.[10][10][11]Scalero R, Tepedelenlioglu N. IEEE Trans. on SP, 1992, SP-40(1): 202-210.[12]黃德雙.雷達目標一維象識別技術的研究:[博士論文].西安電子科技大學,1992.12.[13]西安電子科技大學電子工程研究所目標識別研究組.雷達目標特性測試,識別與分類研究報告,1992.12.[14]Renals S, Rohwer R. Phoneme Classification Experiments Using Radial Basis Functions, IJCNN, Vo1.1, Washington DC: 1989, 461-467.[15]Vrchovnik G, Carter C R, Haykin S. Radial Basis Function Classification of Impulse Radar Waveforms, IJCNN, Vo1.1, San Diego: 1990, 45-50.[16]Ruck D W, Rogers S K, Kahrisky, M, et al. IEEE Trans. on NN, 1990, NN-1(4): 296-298.[17]Parzen E. Ann[J].Math. Statist.1962, 33(6):1065-1076[18]Duin R P W. IEEE Trans. on Compt., 1976, C-25(8): 1175-1179.
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出版歷程
  • 收稿日期:  1993-06-14
  • 修回日期:  1991-02-14
  • 刊出日期:  1995-01-19

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