山东大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (3): 127-133.doi: 10.6040/j.issn.1672-3961.0.2017.423
曹雅,邓赵红*,王士同
CAO Ya, DENG Zhaohong*, WANG Shitong
摘要： 径向基函数(radial basis function, RBF)神经网络是一种高效的前馈式神经网络。它结构简单,具有良好的泛化能力,已经被广泛的应用于数据分类中。但是对于一些特殊的分类场景,如单调数据场景,神经网络还未充分发挥其潜能。针对此,提出单调径向基函数神经网络(monotonic radial basis function neural network, MC-RBF)。MC-RBF引入Tikhonov 正则化方法确保优化问题解的唯一性与有界性。试验结果表明,在处理具有单调性的数据集时,MC-RBF比原始的RBF神经网络具有更好的分类性能。
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