一种新的径向基概率神经网络模型(Ⅱ):模型分析

A NEW MODEL ON RADIAL BASIS PROBABILISTIC NEURAL NETWORKS （Ⅱ）:CASE STUDIES

摘要: 文中从线性代数的角度，对已提出的径向基概率神经网络（ＲＢＰＮＮ）的映射特性作了详尽的分析，最后，使用实测的飞机目标数据验证．该模型用作模式分类器是十分有效的．

Abstract: The mapping properties of the radial basis probabilistic neural network model （RBPNN） are analyzed in detail from the viewpoint of linear algebra. The one dimensional cross images of five types of aircrafts obtained in a microwave darkroom are used to verify the classification performances of the proposed network. The experimental results show that this new model is very effective and feasible for labeled pattern classification.