A Recurrent RBF Neural Network Based on Modified Gravitational Search Algorithm

被引：0
|
作者：
Ren, Zhongming [1 ,2 ]
Li, Wenjing [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：
2018 CHINESE AUTOMATION CONGRESS (CAC) | 2018年
关键词：
gravitational search algorithm; recurrent RBF neural network; optimization; fast convergence speed;
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：
Recently, Gravitational search algorithm (GSA) was considered as one method for optimizing functions and solving real problems. For the sake of better adjust the values of recurrent RBF neural network (RRBFNN) to make the network achieve better performance, the MGSA is essential in this article. The advised work achieves a better compromise between exploration and development. At the same time, by increasing the guidance of the global optimal particle, the problem that the gravitational search algorithm converges slowly in the later iteration is solved. The Experiment found that the network has better convergence speed and better test accuracy than the RRBFN optimized by the conventional optimization algorithm.
引用
收藏
页码：4079 / 4083
页数：5
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