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Volume 21 Issue 1
Jan.  1999
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Tongjing SUN, Tong LIU, Yang YANG. Sparse Representation Classification Method for Active Sonar Target Based on Multi-order Fractional Fourier Domain Feature Fusion[J]. Journal of Electronics & Information Technology, 2021, 43(3): 809-816. doi: 10.11999/JEIT200634
Citation: Liu Guangyuan, Liao Xiaofeng, Yu Juebang, Qiu Yuhui. GENERALIZATION STUDY FOR A ROBUST ESTIMATION METHOD OF NEURAL NETS[J]. Journal of Electronics & Information Technology, 1999, 21(1): 128-131.

GENERALIZATION STUDY FOR A ROBUST ESTIMATION METHOD OF NEURAL NETS

  • Received Date: 1997-04-22
  • Rev Recd Date: 1998-06-05
  • Publish Date: 1999-01-19
  • In this paper, the Cauchy function is taken as a new target function of neural network accordings to the robustness theorem of statistics. Under the same network parameter conditions the BP net is trained using both mean squresand Cauchy target function firstly, then the net is tested by data sets including small Gaussian noises and outliers separately. Simulation results indicate that the network has both faster convergence speed and better performance against outliers after learning with robust target function.
  • Huber P J. Robust estimation of a location parameter[J].The Annals of Mathematical Statistics.1964, 35:73-101[2]Andrews D A. Robust method for mutiple linear regression[J].Technometrics.1974, 16:523-531[3]Liano K. Robust error measure for supervised neural network learning with outliers. IEEE Trans. on Neural Networks, 1996, NN-7(1): 244-250.[4]Chen D S, Jain R C. A robust back propagation learning algorithm for function appoximation. IEEE Trans. on Neural Networks, 1994, NN-5(3): 467-469.[5]Oja E, Wang L. Robust fitting by nonlinear neural units[J].Neural Networks.1996, 9(3):435-444[6]Humpert B K. Improving back propagation with a new error function. Neural Nerworks, 1994, 7(R)- 1101-1149.[7]陈希孺, 王松桂. 近代实用回归分析. 南宁:广西人民出版社, 1984: 301-321.[8]廖晓峰, 刘光远, 虞厥邦.几种误差估计器的稳健BP:理论与算法.信号处理,1997, 13(3): 235-240.
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