Types of Recurrent Neural Networks For Non-linear Dynamic System Modelling

被引:0
作者
Solovyeva, Elena B. [1 ]
机构
[1] St Petersburg Electrotech Univ LETI, Theoret Elect Engn Dept, St Petersburg, Russia
来源
PROCEEDINGS OF 2017 XX IEEE INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND MEASUREMENTS (SCM) | 2017年
关键词
non-linear model; neural network; approximation; dynamic system;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Recurrent neural networks are represented as nonlinear models of dynamic systems. This kind of neural networks is divided into two groups, which are globally and locally recurrent neural networks. Some types are distinguished among globally recurrent networks. The major approximation properties and features of every distinguished type are emphasized. The represented analysis is useful for choosing the neural network structure a priori (prior to its training or constructing the mathematical model of the nonlinear system).
引用
收藏
页码:252 / 255
页数:4
相关论文
共 7 条
[1]  
Chaos CNN, 2013, MEMRISTORS FESTSCHRI
[2]  
Chua L. O., 2002, Cellular neural networks and visual computing: foundations and applications
[3]   CELLULAR NEURAL NETWORKS - APPLICATIONS [J].
CHUA, LO ;
YANG, L .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS, 1988, 35 (10) :1273-1290
[4]   CELLULAR NEURAL NETWORKS - THEORY [J].
CHUA, LO ;
YANG, L .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS, 1988, 35 (10) :1257-1272
[5]  
Graupe D., 2013, World Sci, V7
[6]  
Haykin S., 2009, NEURAL NETWORKS LEAR
[7]  
Patan K, 2008, LECT NOTES CONTR INF, V377, P1