| 一种基于改进k-means的RBF神经网络学习方法 |
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| 引用本文： | 庞振,徐蔚鸿.一种基于改进k-means的RBF神经网络学习方法[J].计算机工程与应用,2012,48(11):161-163,184. |
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| 作者姓名： | 庞振 徐蔚鸿 |
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| 作者单位： | 长沙理工大学计算机与通信工程学院,长沙,410004 |
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| 基金项目： | 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） |
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| 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。
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| 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 |
Learning algorithm for RBF neural networks based on improved k-means algorithm |
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| PANG Zhen
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XU Weihong.Learning algorithm for RBF neural networks based on improved k-means algorithm[J].Computer Engineering and Applications,2012,48(11):161-163,184. |
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| Authors: | PANG Zhen XU Weihong |
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| Affiliation: | School of Computer ＆ Communication Engineering, Changsha University of Science ＆ Technology, Changsha 410004, China |
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| 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. |
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| Keywords: | subtractive clustering algorithm k-means algorithm Radial Basis Function（RBF）neural network gradient descent algorithm |
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