DocumentCode
353245
Title
An estimate of the number of samples to convergence for critic algorithms
Author
Hrycej, Tomas
Author_Institution
DaimlerChrysler AG, Ulm, Germany
Volume
3
fYear
2000
fDate
2000
Firstpage
227
Abstract
Simplified critic based neurocontrol algorithms are analyzed for expected number of samples to convergence. It is shown that there is a fundamental difference in the complexity behavior between the batch and the incremental algorithm, and between the algorithm with and without an explicit plant model. The batch algorithm using a plant model is superior to other variants
Keywords
computational complexity; convergence of numerical methods; learning (artificial intelligence); neurocontrollers; optimisation; probability; state-space methods; batch algorithm; complexity behavior; convergence; critic algorithms; incremental algorithm; neurocontrol; optimization; probability; state space; Algorithm design and analysis; Convergence; Cost function; Delay effects; Dynamic programming; Neural networks; Probability distribution; Sampling methods; State-space methods; Table lookup;
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.2000.861308
Filename
861308
Link To Document