%0 Journal Article
%T 基于单调约束的径向基函数神经网络模型<br>An radial basis function neural network model based on monotonic constraints
%A 曹雅
%A 邓赵红
%A 王士同&lt
%A br&gt
%A CAO Ya
%A DENG Zhaohong
%A WANG Shitong
%J 山东大学学报（工学版）
%D 2018
%R 10.6040/j.issn.1672-3961.0.2017.423
%X 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。<br>Abstract: Radial basis function(RBF)neural network was a type of efficient feedforward neural network, which had simple structure and good generalization ability. It had been widely used in data classification. However, for some special classification scenarios, such as the scenarios of dealing with the monotonic data, RBF neural network could not fully realize its potential. For this challenge, monotonic radial basis function neural network(MC-RBF)was proposed. The model added a prior knowledge about monotonicity which was expressed in terms of inequality based on partial order of training data. The Tikhonov regularization was introduced to MC-RBF to ensure the uniqueness and boundedness of the solution of the optimization problem. The experimental results showed that MC-RBF had better classification performance than the classical RBF neural network when dealing with monotonic datasets
%K 径向基函数神经网络
%K 数据分类
%K Tikhonov 正则化
%K 单调约束
%K 分类性能
%K &lt
%K br&gt
%K monotonic constraint
%K data classification
%K Tikhonov regularization
%K radial basis function neural network
%K classification performance
%U http://gxbwk.njournal.sdu.edu.cn/CN/10.6040/j.issn.1672-3961.0.2017.423