摘要
提出了一种新的RBF神经网络的训练方法,采用动态K-均值方法对RBF神经网络的隐层中心值和宽度进行了优化,用代数算法训练隐层和输出层之间的权值。在对非线性函数进行逼近的仿真中,验证了该算法的有效性。
A new training method is presented for RBF neural network.Moving k-means clustering algorithm is used to optimize the centers and widths of RBF algebraic algorithm is used to train the weights between hidden layer and output layer.The approach is used in the approximation of nonlinear function.And the result indicates it＇s effective.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第5期96-98,共3页
Computer Engineering and Applications
基金
北京市教育委员会科技发展计划重点项目(No.KZ200710028014)
