基于径向基函数和改进径向基函数网络的雷达目标分类

Liu Guo-sui, Wang Yunhong, Yang Chunling, Z. Dequan
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引用次数: 7

摘要

讨论了径向基函数神经网络在雷达目标分类中的应用。为了提高分类率,提出了改进径向基函数(MRBF)神经网络结构。本文讨论了MRBF网络的两种类型,即MRBF1网络和MRBF2网络。通过理论和计算机仿真,我们发现MRBF网络的性能优于RBF网络,MRBF2网络的分类率高于MRBF1网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Radar target classification based on radial basis function and modified radial basis function networks
This paper discusses the radial basis function (RBF) neural networks used in the radar target classification. To enhance the classification rate, the structure of the modified radial basis function (MRBF) neural network is proposed. Two kinds of MRBF networks which are called the MRBF1 network and the MRBF2 network are discussed in this paper. From the theory as well as computer simulations, we find that the performance of the MRBF network is superior to the RBF network and the MRBF2 network gets higher classification rate than the MRBF1 network.
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