A pulse neural network learning algorithm for POMDP environment

Koichiro Takita, Masafumi Hagiwara

研究成果: Paper査読

2 被引用数 (Scopus)

抄録

In this paper, we proposed a new pulse neural network model and its reinforcement learning algorithm. The main purpose of this model is to utilize pulse neuron's ability to handle sequential inputs in partially observable Markov decision process (POMDP). Its performance is confirmed by computer simulation.

本文言語English
ページ1643-1648
ページ数6
出版ステータスPublished - 2002
イベント2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States
継続期間: 2002 5月 122002 5月 17

Other

Other2002 International Joint Conference on Neural Networks (IJCNN '02)
国/地域United States
CityHonolulu, HI
Period02/5/1202/5/17

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

フィンガープリント

「A pulse neural network learning algorithm for POMDP environment」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル