A Modified K-means Algorithm based RBF Neural Network and Its Application in Time Series Modelling

摘要：

　　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 Kmeans 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.

关键词： K-means Algorithm Initial Cluster Centers RBF Neural Network Chaotic Time Series

作者: Yiping Jiao Yu Shen Shumin Fei

作者单位: School of Automation Southeast University Nanjing,China

会议类型: 国际会议

会议名称: The 14th International Symposium on Distributed Computing and Applications to Business,Engineering and Science(DCABES 2015)(第十四届分布式计算及其应用国际学术研讨会)

会议地点: 贵阳

会议语种:英文

页码: 481-484

在线出版日期: 2015-08-18（万方平台首次上网日期，不代表论文的发表时间）