Computer Engineering and Applications ›› 2012, Vol. 48 ›› Issue (11): 161-163.

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

PANG Zhen, XU Weihong   

  1. School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha 410004, China
  • Online:2012-04-11 Published:2012-04-16


庞  振,徐蔚鸿   

  1. 长沙理工大学 计算机与通信工程学院,长沙 410004

Abstract: 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.

Key words: subtractive clustering algorithm, k-means algorithm, Radial Basis Function(RBF) neural network, gradient descent algorithm

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。

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