Performance optimization of function localization neural network by using reinforcement learning

Takafumi Sasakawa, Jinglu Hu, Kotaro Hirasawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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 selforganizing 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).

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages1314-1319
Number of pages6
DOIs
Publication statusPublished - 2005 Dec 1
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: 2005 Jul 312005 Aug 4

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2

Conference

ConferenceInternational Joint Conference on Neural Networks, IJCNN 2005
CountryCanada
CityMontreal, QC
Period05/7/3105/8/4

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Sasakawa, T., Hu, J., & Hirasawa, K. (2005). Performance optimization of function localization neural network by using reinforcement learning. In Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005 (pp. 1314-1319). [1556044] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2). https://doi.org/10.1109/IJCNN.2005.1556044