Reinforcement learning algorithm with network extension for pulse neural network

被引：0
|
作者：
Takita, M [1 ]
Osana, Y [1 ]
Hagiwara, M [1 ]
机构：
[1] Keio Univ, Fac Sci & Technol, Kohoku Ku, Yokohama, Kanagawa 2238522, Japan
来源：
SMC 2000 CONFERENCE PROCEEDINGS: 2000 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOL 1-5 | 2000年
关键词：
reinforcement learning; pulse neural network;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：2586 / 2591
页数：6
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