A Recurrent RBF Neural Network Based on Adaptive Optimum Steepest Descent Learning Algorithm

被引：0
|
作者：
Ma, Shijie [1 ,2 ]
Yang, Chili [1 ,2 ]
Qiao, Junfei [1 ,2 ]
机构：
[1] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
[2] Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
来源：
PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017) | 2017年
基金：
中国国家自然科学基金;
关键词：
Nonlinear system modeling; recurrent RBF neural network; AOSD learning algorithm; fast convergence; NONLINEAR-SYSTEMS; STABILITY;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In order to overcome the defects of gradient descent (GD) algorithm which lead to slow convergence and easy to fall into local minima, this paper proposes an adaptive optimum steepest descent (AOSD) learning algorithm which is used for the recurrent radial basis function (RRBF) neural network. Compared with traditional GD algorithm, the adaptive learning rate is integrated into the AOSD learning algorithm in order to accelerate the convergence speed of training algorithm and improve the network performance of nonlinear system modeling. Several comparisons show that the proposed RRBF has faster convergence speed and better prediction performance.
引用
收藏
页码：3942 / 3947
页数：6
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