现代电子技术Issue(7)：114-117,4.
GRNN与BPNN的函数逼近性能对比研究
Comparative study on function approximation performances of GRNN and BPNN
摘要
Abstract
To study the nonlinear function approximation performances of GRNN and BPNN,the learning processes of GRNN and BPNN based on LM optimization algorithm improvement are illustrated mathematically in this paper. Then GRNN and BPNN were established with computer programming. A given nonlinear function was approximated by the two neural net-works respectively. The simulation results indicate that when the numbers of training samples are the same and the networks are small or medium-sized,GRNN has higher precision,faster convergence speed,and better approximation ability than BPNN. Thus GRNN is a good method to solve the problem of nonlinear function approximation.关键词
广义回归神经网络/反向传播神经网络/函数逼近/逼近能力对比/仿真Key words
GRNN/BPNN/function approximation/approximation capability comparison/simulation分类
信息技术与安全科学引用本文复制引用
丁硕,常晓恒,巫庆辉..GRNN与BPNN的函数逼近性能对比研究[J].现代电子技术,2014,(7):114-117,4.基金项目
国家自然科学基金(61104071)；辽宁省教育厅科学研究一般项目 （61104071）
