TY - JOUR
T1 - Reinforcement learning algorithm with network extension for pulse neural network
AU - Takita, Koichiro
AU - Osana, Yuko
AU - Hagiwara, Masafumi
PY - 2000/12/1
Y1 - 2000/12/1
N2 - In this paper, we propose a new hierarchical pulse neural network and its reinforcement learning algorithm with network extension. The proposed pulse neural network has three layers, and all of the neurons are pulse neurons. This network learns relations between input pulse sequences and the desired outputs by updating connection weights and by adding neurons dynamically. We carried out the computer simulation to confirm the performance of the proposed algorithm.
AB - In this paper, we propose a new hierarchical pulse neural network and its reinforcement learning algorithm with network extension. The proposed pulse neural network has three layers, and all of the neurons are pulse neurons. This network learns relations between input pulse sequences and the desired outputs by updating connection weights and by adding neurons dynamically. We carried out the computer simulation to confirm the performance of the proposed algorithm.
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M3 - Conference article
AN - SCOPUS:0034511471
VL - 4
SP - 2586
EP - 2591
JO - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
JF - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
SN - 0884-3627
T2 - 2000 IEEE International Conference on Systems, Man and Cybernetics
Y2 - 8 October 2000 through 11 October 2000
ER -