DocumentCode :
423667
Title :
Reinforcement learning in multiresolution object recognition
Author :
Iftekharuddin, Khan M. ; Widjanarko, Taufiq
Author_Institution :
Dept. of Electr. & Comput. Eng., Memphis Univ., USA
Volume :
2
fYear :
2004
fDate :
25-29 July 2004
Firstpage :
1085
Abstract :
In this work, we propose an adaptive automatic target recognition (ATR) technique that exploits reinforcement learning (RL) for multiresolution object recognition. The RL structure is the implementation of neuro-dynamic programming (NDP) for the critic and action networks. The critic network calculates the cost-to-go function J* based on a simplistic ATR plant that involves multiresolution images as the input state variable. The calculation of this function, J* includes the role of the reinforcement signal in the critic network. Output of this critic stage is fed back to update the weights of both action and critic networks respectively. Our simulation results suggest that RL may be successfully integrated into an adaptive multiresolution ATR framework.
Keywords :
dynamic programming; image resolution; learning (artificial intelligence); neural nets; object recognition; action networks; adaptive automatic target recognition; adaptive multiresolution ATR; cost-to-go function; multiresolution image recognition; multiresolution object recognition; neurodynamic programming; reinforcement learning; Artificial neural networks; Biological systems; Computer hacking; Image processing; Image resolution; Intelligent systems; Learning; Object recognition; Signal resolution; Target recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
ISSN :
1098-7576
Print_ISBN :
0-7803-8359-1
Type :
conf
DOI :
10.1109/IJCNN.2004.1380085
Filename :
1380085
Link To Document :
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