摘要:
提出一种基于增强的全局K’-means算法(EGK’M)-RBF网络的建模方法,该方法采用作者提出的 EGK’M来确定RBF网络隐含层的结构,包括隐含层中心个数、中心位置以及隐含层扩展常数,采用KPCA提取非线性特征信息,实现辅助变量的二次选择。并与基于PCA和EGK’M-RBF网络模型、基于 FONT-SIZE: 9pt, mso-ascii-theme-font: minor-latin, mso-fareast-theme-font: minor-fareast, mso-bidi-font-family: 宋体, mso-ansi-language: EN-US, mso-fareast-language: ZH-CN, mso-bidi-language: AR-SA, mso-fareast-font-family: 宋体, mso-hansi-font-family: 宋体" lang=EN-US>KFONT-SIZE: 9pt, mso-ascii-font-family: Calibri, mso-ascii-theme-font: minor-latin, mso-fareast-theme-font: minor-fareast, mso-bidi-font-family: 宋体, mso-ansi-language: EN-US, mso-fareast-language: ZH-CN, mso-bidi-language: AR-SA">’FONT-SIZE: 9pt, mso-ascii-theme-font: minor-latin, mso-fareast-theme-font: minor-fareast, mso-bidi-font-family: 宋体, mso-ansi-language: EN-US, mso-fareast-language: ZH-CN, mso-bidi-language: AR-SA, mso-fareast-font-family: 宋体, mso-hansi-font-family: 宋体" lang=EN-US>-meansFONT-SIZE: 9pt, mso-ascii-font-family: Calibri, mso-ascii-theme-font: minor-latin, mso-fareast-theme-font: minor-fareast, mso-bidi-font-family: 宋体, mso-ansi-language: EN-US, mso-fareast-language: ZH-CN, mso-bidi-language: AR-SA">算法FONT-SIZE: 9pt, mso-ascii-theme-font: minor-latin, mso-fareast-theme-font: minor-fareast, mso-bidi-font-family: 宋体, mso-ansi-language: EN-US, mso-fareast-language: ZH-CN, mso-bidi-language: AR-SA, mso-fareast-font-family: 宋体, mso-hansi-font-family: 宋体" lang=EN-US>;FONT-SIZE: 9pt, mso-ascii-font-family: Calibri, mso-ascii-theme-font: minor-latin, mso-fareast-theme-font: minor-fareast, mso-bidi-fo
Abstract:
A modeling method by radial basis function(RBF)network based on enhanced global K’-means algorithm(EGK’M)was presented.An EGK’M algorithm was proposed to determine the hidden layer structure of RBF network,including the number of hidden layer nodes,the position of each center and the width of basis function.KPCA algorithm was used to extract non-linear feature information and to achieve secondary selection of auxiliary variable.The obtained model was compared with the model based on principle components analysis with EGK’M-RBF and the model based on KPCA with RBF network based on K’-means algorithm.Experiment results demonstrate that the model proposed in this paper gives better predictive ability,smaller absolute error and mean square error.
Key words: FONT-SIZE: 9pt, mso-ascii-font-family: Calibri, mso-ascii-theme-font: minor-latin, mso-fareast-theme-font: minor-fareast, mso-bidi-font-family: 宋体, mso-ansi-language: EN-US, mso-fareast-language: ZH-CN, mso-bidi-language: AR-SA">改进的全局FONT-SIZE: 9pt, mso-ascii-theme-font: minor-latin, mso-fareast-theme-font: minor-fareast, mso-bidi-font-family: 宋体, mso-ansi-language: EN-US, mso-fareast-language: ZH-CN, mso-bidi-language: AR-SA, mso-fareast-font-family: 宋体, mso-hansi-font-family: 宋体" lang=EN-US>KFONT-SIZE: 9pt, mso-ascii-font-family: Calibri, mso-ascii-theme-font: minor-latin, mso-fareast-theme-font: minor-fareast, mso-bidi-font-family: 宋体, mso-ansi-language: EN-US, mso-fareast-language: ZH-CN, mso-bidi-language: AR-SA">’FONT-SIZE: 9pt, mso-ascii-theme-font: minor-latin, mso-fareast-theme-font: minor-fareast, mso-bidi-font-family: 宋体, mso-ansi-language: EN-US, mso-fareast-language: ZH-CN, mso-bidi-language: AR-SA, mso-fareast-font-family: 宋体, mso-hansi-font-family: 宋体" lang=EN-US>-meansFONT-SIZE: 9pt, mso-ascii-font-family: Calibri, mso-ascii-theme-font: minor-latin, mso-fareast-theme-font: minor-fareast, mso-bidi-font-family: 宋体, mso-ansi-language: EN-US, mso-fareast-language: ZH-CN, mso-bidi-language: AR-SA">算法FONT-SIZE: 9pt, mso-ascii-theme-font: minor-latin, mso-fareast-theme-font: minor-fareast, mso-bidi-font-family: 宋体, mso-ansi-language: EN-US, mso-fareast-language: ZH-CN, mso-bidi-language: AR-SA, mso-fareast-font-family: 宋体, mso-hansi-font-family: 宋体" lang=EN-US>;FONT-SIZE: 9pt, mso-ascii-font-family: Calibri, mso-ascii-theme-font: minor-latin, mso-fareast-theme-font: minor-fareast, mso-bidi-fo
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