一种基于改进k-means的RBF神经网络学习方法  

Learning algorithm for RBF neural networks based on improved k-means algorithm

作　　者：庞振[1] 徐蔚鸿[1] 

机构地区：[1]长沙理工大学计算机与通信工程学院,长沙410004

出　　处：《计算机工程与应用》2012年第11期161-163,184,共4页Computer Engineering and Applications

基　　金：教育部重点科研基金项目(No.208098);湖南省教育厅重点项目(No.07A056)

摘　　要：针对传统RBF神经网络学习算法构造的网络分类精度不高,传统的k-means算法对初始聚类中心的敏感,聚类结果随不同的初始输入而波动。为了解决以上问题,提出一种基于改进k-means的RBF神经网络学习算法。先用减聚类算法优化k-means算法,消除聚类的敏感性,再用优化后的k-means算法构造RBF神经网络。仿真结果表明了该学习算法的实用性和有效性。Aiming at the low classification accuracy of network trained by traditional RBF neural networks learning algorithm,the traditional k-means algorithm has sensitivity to the initial clustering center.To solve these problems,an improved learning algorithm based on improved k-means algorithm is proposed.The new algorithm optimizes k-means algorithm with subtractive clustering algorithm to eliminate the clustering sensitivity,and constructs RBF neural networks with the optimized k-means algorithm.The simulation results demonstrate the practicability and the effectiveness of the new algorithm.

关 键 词：减聚类算法 K-MEANS算法 径向基函数(RBF)神经网络 梯度下降法 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]