基于径向基函数网络的雷达目标一维像识别技术研究

黄德双; 保铮

基于径向基函数网络的雷达目标一维像识别技术研究

黄德双,
保铮

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出版历程
- 收稿日期: 1993-06-14
- 修回日期: 1991-02-14
- 刊出日期: 1995-01-19

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

摘要

摘要: 本文研究了利用递推最小二乘(RLS)方法训练的径向基函数网络(RBFN)用于雷达目标一维像的识别与分类问题。证明了RBFN与Parzen窗函数概率密度估计的等价性,指出RBFN隐层单元传输函数可以推广到一般的Psrzen概率核函数或势函数形式。还就高斯、三角、双指数三种核函数讨论了径向基函数网络形状参数、递推最小二乘算法的遗忘因子对识别结果的影响以及与网络训练时间的关系。
- 模式识别; 核函数; 形状参数; 遗忘因子; 一维像; 径向基函数; 递推最小二乘法
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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