DocumentCode
352972
Title
An extended RTRL training algorithm using Hessian matrix
Author
Coelho, Pedro H G
Author_Institution
State Univ. of Rio de Janeiro, Brazil
Volume
4
fYear
2000
fDate
2000
Firstpage
563
Abstract
An extended real time recurrent learning (RTRL) algorithm using Hessian matrix is proposed. The algorithm is suitable for small fully recurrent neural networks present in several applications. Simulation results indicate that the training algorithm is fast
Keywords
Hessian matrices; Newton method; convergence of numerical methods; learning (artificial intelligence); real-time systems; recurrent neural nets; transfer functions; Hessian matrix; Newton method; convergence; real time recurrent learning; recurrent neural networks; Adaptive control; Concurrent computing; Feedforward neural networks; Hardware; Information processing; Neural networks; Neurons; Newton method; Recurrent neural networks; Signal processing algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location
Como
ISSN
1098-7576
Print_ISBN
0-7695-0619-4
Type
conf
DOI
10.1109/IJCNN.2000.860831
Filename
860831
Link To Document