Adaptive learning approach of integrating evolution fuzzy-neural networks and Q-learning for mobile robots

Hong Jian Zhon, Wei Min Hsieh, Yih-Guang Leu*, Chin-Ming Hong

*Corresponding author for this work

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

Abstract

In the paper, an adaptive learning approach of integrating evolution fuzzy-neural networks and Q-learning is developed so that a mobile robot can adapt itself to a real and complex environment. Specifically, based on Q-value and an evolution method that adjusts their parameter values of the fuzzy-neural networks, the mobile robot evolves better strategies to adapt to the environment. However, in most studies of evolution learning, the learning of mobile robots often requires a simulator and an enormous amount of evolution time so as to perform a task. Therefore, we are to integrate Q-learning into the evolution fuzzy-neural networks to avoid the requirement of the simulator. Experiment results of a mobile robot illustrate the performance of the proposed approach.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008
Pages1902-1906
Number of pages5
DOIs
Publication statusPublished - 2008 Nov 7
Event2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008 - Hong Kong, China
Duration: 2008 Jun 12008 Jun 6

Other

Other2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008
Country/TerritoryChina
CityHong Kong
Period2008/06/012008/06/06

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Artificial Intelligence
  • Applied Mathematics

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