Radial-basis-function neural network based on fast recursive algorithm and its application

被引：0
|
作者：
Du, Da-Jun [1 ]
Fei, Min-Rui [1 ]
Li, Li-Xiong [1 ]
机构：
[1] Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronical Engineering and Automation, Shanghai University, Shanghai 200072, China
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2008年 / 25卷 / 05期
关键词：
Orthogonal functions - Time series;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Considering the difficulty in selecting the numbers and determining the locations of the centers of radial basis functions (RBF) in the RBF neural network (RBFNN), a novel RBFNN is proposed based on the fast recursive algorithm (FRA). Using FRA, we can determine the numbers and locations of the centers, and derive the weights between the hidden layer and the output layer. The new RBFNN is used to fit a single-variable function curve and predict the Mackey-Glass chaotic time series. The simulation results demonstrate the effectiveness and practicability.
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页码：827 / 830
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