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
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;
Conference_Titel :
Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on
Conference_Location :
Hefei
Print_ISBN :
0-7803-5995-X
DOI :
10.1109/WCICA.2000.863342