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Performance optimization of function localization neural network by using reinforcement learning

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3 Author(s)
Sasakawa, T. ; Graduate Sch. of Inf., Production & Syst., Waseda Univ., Tokyo, Japan ; Jinglu Hu ; Hirasawa, K.

According to Hebb's cell assembly theory, the brain has the capability of function localization. On the other hand, it is suggested that the brain has three different learning paradigms: supervised, unsupervised and reinforcement learning. Inspired by the above knowledge of brain, we present a self-organizing function localization neural network (FLNN), that contains supervised, unsupervised and reinforcement learning paradigms. In this paper, we concentrate our discussion mainly on applying a simplified reinforcement learning called evaluative feedback to optimization of the self-organizing FLNN. Numerical simulations show that the self-organizing FLNN has superior performance to an ordinary artificial neural network (ANN).

Published in:

Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on  (Volume:2 )

Date of Conference:

31 July-4 Aug. 2005