基于改进K-Means算法的RBF神经网络及其在时间序列建模中的应用

2015 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2015-08-18 DOI:10.1109/DCABES.2015.126

Yiping Jiao, Yu-zhi Shen, Shu-ming Fei

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引用次数: 1

摘要

本文讨论了一种改进的基于k均值的RBFNN。为了提高RBFNN的性能，提出了K-means算法的初始聚类中心选择策略。该算法以样本的宽度偏好子集作为icc，利用贪心策略覆盖样本空间。结果表明，该算法能显著提高RBFNN在混沌时间序列建模中的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Modified K-Means Algorithm Based RBF Neural Network and Its Application in Time Series Modelling

In this paper, a modified K-means based RBFNN is discussed. To improve the performance of RBFNN, an initial cluster centers (ICCs) selection strategy is proposed for K-means algorithm. The algorithm takes breadth preferred subset of samples as ICCs to cover the sample space using greedy strategy. The results shows that the proposed algorithm can improve the performance of RBFNN remarkably in chaotic time series modelling.

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2015 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

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