摘要
径向基函数(RBF)神经网络因其结构简单而被广泛地用于非线性函数近似和数据分类。RBF神经网络的隐层神经元的作用可解释成从非线性可分空间向线性可分空间映射的函数。本文提出一种基于能量分布的RBF神经网络OLS算法。实验结果表明我们的方法比标准的OLS其性能更好。
: Due to its structural simplicity, the radial basis function (RBF)neural network has been widely used for approximation and classification. The role of hidden layer neurons of a RBF neural network can be interpreted as a function which maps input patterns from a nonlinear separable space to a linear separable space. In the present study, we use OLS algorithm based on energy distribution to train RBF. The experiment results indicate that the performance of the proposed method is better than that of standard OLS.
出处
《计算机科学》
CSCD
北大核心
2004年第4期133-134,156,共3页
Computer Science
基金
重庆市科技计划项目(2001-6810)的资助
