基于改进BP网络和RBF网络识别能力比较研究

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中图分类号：TP393 文献标识码：A 文章编号：1674-0033（2025）04-0039-08

Abstract：Inview of thedifferences in the recognitionabilitiesof BP neural networksand RBF networks in pixel-level data sample patern recognition，an improved BP neural network （adopting an adaptive learning rate and an additional momentum method） was constructed and compared with an RBF neural network. Three sets of experiments were designed： adding 5%～50% random noise，partially occluding letters，and introducing specific contour noise.The recognition performance was analyzed using confusion matrices.The results show that in the presence of random noise below 30% ，the recognition rates of both networksreach 100% .When the noise increases to 40% ，the recognition rate of the improved BP neural network drops to 84.61% ，while the RBF neural network still maintains 100% .Inthe face of partial occlusion，both networks perform well；however，under the interference of contour noise，therecognition rate of the RBF neural network （84.62%） issignificantly higher than that of the improved BP neural network （65.38% ）.The results confirm that the RBF network has stronger generalization ability in complex noise scenarios and provides a better option for low- dimensional data recognition.

Key words：BP neural network; RBF neural network; pattern recognition

人工神经网络于20世纪80年代蓬勃兴起， 式的差异，神经网络衍生出众多类型。（剩余9668字）