基于径向基函数网络的雷达目标一维像识别技术研究

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

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α、递推最小二乘算法的遗忘因子λ对识别结果的影响以及λ与网络训练时间的关系。

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 a in RBFN and the forgotten factor λ 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 λ and the training time in RBFN.