A Study on Three Kinds of New Learning Subspace Methods for Pattern Recognition

HuanR Deshuang

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A Study on Three Kinds of New Learning Subspace Methods for Pattern Recognition

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- A Study on Three Kinds of New Learning Subspace Methods for Pattern Recognition
- Acta Electronica Sinica Issue 7, (1995)
- 作者机构：
  
  北京理工大学电子工程系
- 作者简介：
- 基金信息：
- DOI：
  CLC： O235
- Published：1995
- 稿件说明：
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[1]黄德双.三种新的学习子空间模式识别方法的研究[J].电子学报,1995(07):25-30.

HuanR Deshuang. A Study on Three Kinds of New Learning Subspace Methods for Pattern Recognition[J]. Acta Electronica Sinica, 1995, (7).
[1]黄德双.三种新的学习子空间模式识别方法的研究[J].电子学报,1995(07):25-30. DOI：

HuanR Deshuang. A Study on Three Kinds of New Learning Subspace Methods for Pattern Recognition[J]. Acta Electronica Sinica, 1995, (7). DOI：

摘要

本文提出了最小模、检错平均、前后向平滑三种新的神经网络学习子空间模式识别方法．这些方法在识别率和收敛速度等整体性能上，接近或超过Ｅ．Ｏｊａ的平均学习子空间方法；特别是，前后向平滑学习子空间方法是目前最好的一类学习子空间方法，在模式识别领域，特别是语音识别方面具有广泛的应用前景，本文就舰船目标与箔条杂波利用这些方法进行了若干分类与识别实验，计算机模拟结果证实了这些方法的有效性．

Abstract

This paper presents three kinds of new Neural Networks

Learning Subspace Methods（LSM） for Pattern Recognition such as Mininum Norm（MN）

Detecting Error Averaged（DEA）

Forward and Backward Smoothing（FBS）.Their total performances in recognition rate and convergence speed approach or surpass ALSM’s by E.Oja.Especially

the FBS-LSM is by now best method in LSMs.They have been widely used in Pattern Recognition

especially

in speech classfication.Finally

these methods are applied to the classification of targets-ships and clutters-chaffs

the results of the computers simulation are given

also.

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