Enhancement of self organizing network elements for supervised learning

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

Abstract

We have proposed self-organizing network elements (SONE) as a learning method for robots to meet the requirements of autonomous exploration of effective output, simple external parameters, and low calculation costs. SONE can be used as an algorithm for obtaining network topology by propagating reinforcement signals between the elements of a network. Traditionally, the analysis of fundamental features in SONE and their application to supervised learning tasks were difficult because the learning method of SONE was limited to reinforcement learning. Here the abilities of generalization, incremental learning, and temporal sequence learning were evaluated using a supervised learning method with SONE. Moreover, the proposed method enabled our SONE to be applied to a greater variety of tasks.

Original languageEnglish
Title of host publication2007 IEEE International Conference on Robotics and Automation, ICRA'07
Pages92-98
Number of pages7
DOIs
Publication statusPublished - 2007 Nov 27
Event2007 IEEE International Conference on Robotics and Automation, ICRA'07 - Rome, Italy
Duration: 2007 Apr 102007 Apr 14

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2007 IEEE International Conference on Robotics and Automation, ICRA'07
CountryItaly
CityRome
Period07/4/1007/4/14

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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