信阳师范学院学报（自然科学版）Issue(3)：428-431,4.DOI:10.3969/j.issn.1003-0972.2013.03.030
基于正交最小二乘法的径向基神经网络模型
Radial Basis Function Neural Network Model Based on Orthogonal Least Squares
摘要
Abstract
In order to improve the forecasting accuracy of the neural network model and the computational efficien -cy, the structure of Gaussian radial basis neural network based on orthogonal least squares was constructed and the re -gression models of neural network was given .The center parameters of Gaussian function were determined by the se -quence information of the sample point and the connection weights between the hidden layer and output layer was deter -mined by the recursive computation of the orthogonal least squares .The performances of this method and the other liter -ature method used to forecast the model based on chaotic Lorenz time series were compared in terms of forecasting accu -racy and the recursive time required .The results indicated that the designed model has many advantages such as higher forecasting accuracy and higher computational efficiency .关键词
正交最小二乘法/高斯函数/径向基函数神经网络/网络模型Key words
orthogonal least squares/Gaussian function/radial basis function ( RBF) neural network/network model分类
信息技术与安全科学引用本文复制引用
刘道华,张礼涛,曾召霞,孙文萧..基于正交最小二乘法的径向基神经网络模型[J].信阳师范学院学报（自然科学版）,2013,(3):428-431,4.基金项目
河南省基础与前沿研究项目（122300410310）；河南省教育厅2013年度教师教育课程改革研究重点项目 （）
