Cellular SRN Trained by Extended Kalman Filter Shows Promise for ADP

Cellular simultaneous recurrent neural network has been suggested to be a function approximator more powerful than the MLP's, in particular for solving approximate dynamic programming problems. The 2D maze navigation has been considered as a proof-of-concept task. Present work improves the prev...

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Veröffentlicht in:	The 2006 IEEE International Joint Conference on Neural Network Proceedings S. 506 - 510
Hauptverfasser:	Ilin, R., Kozma, R., Werbos, P.J.
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 2006
Schlagworte:	Cellular networks Convergence Cost function Dynamic programming Electronic mail Equations Learning Navigation Neural networks Recurrent neural networks
ISBN:	9780780394902, 0780394909
ISSN:	2161-4393
Online-Zugang:	Volltext
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