DocumentCode
2343147
Title
Study on dynamic recursive neural network structure and learning algorithm
Author
Tianyun, Shi ; Jia Limin
Author_Institution
Res. Center of Intelligent Control, China Acad. of Railway, Beijing, China
Volume
2
fYear
2000
fDate
2000
Firstpage
813
Abstract
In order to solve the present problem of dynamic recursive neural network such as slow learning speed, low model accuracy and bad application result, several new dynamic recursive network are put forward based on its structure. The approach of neural network automatic design based on the integration of self-adaptive evolutionary strategy and improved backpropagation algorithm is also advanced to realize the rapid evolution of network structure, weights and self feedback parameter in the same time. The actual application of system modeling shows that the advanced dynamic recursive network and learning algorithm is feasible and perfect
Keywords
backpropagation; evolutionary computation; recurrent neural nets; back propagation algorithm; backpropagation algorithm; dynamic recursive neural network structure; learning algorithm; learning speed; model accuracy; network weights; self feedback parameter; self-adaptive evolutionary strategy; Algorithm design and analysis; Intelligent control; Modeling; Neural networks; Neurofeedback; Rail transportation;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on
Conference_Location
Hefei
Print_ISBN
0-7803-5995-X
Type
conf
DOI
10.1109/WCICA.2000.863342
Filename
863342
Link To Document