Open access peer-reviewed chapter

Genetic Reinforcement Learning Algorithms for On-line Fuzzy Inference System Tuning "Application to Mobile Robotic"

By Abdelkrim Nemra, and and Hacene Rezine

Published: October 1st 2008

DOI: 10.5772/5847

Downloaded: 2321

© 2008 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike-3.0 License, which permits use, distribution and reproduction for non-commercial purposes, provided the original is properly cited and derivative works building on this content are distributed under the same license.

How to cite and reference

Link to this chapter Copy to clipboard

Cite this chapter Copy to clipboard

Abdelkrim Nemra, and and Hacene Rezine (October 1st 2008). Genetic Reinforcement Learning Algorithms for On-line Fuzzy Inference System Tuning "Application to Mobile Robotic", Robotics, Automation and Control Pavla Pecherková, IntechOpen, DOI: 10.5772/5847. Available from:

Embed this chapter on your site Copy to clipboard

<iframe src="http://www.intechopen.com/embed/robotics-automation-and-control/genetic_reinforcement_learning_algorithms_for_on-line_fuzzy_inference_system_tuning_application" />

Embed this code snippet in the HTML of your website to show this chapter

chapter statistics

2321total chapter downloads

More statistics for editors and authors

Login to your personal dashboard for more detailed statistics on your publications.

Access personal reporting

Related Content

This Book

Next chapter

Control of Redundant Submarine Robot Arms under Holonomic Constraints

By E. Olgu&#237;n-D&#237;az, V. Parra-Vega and D. Navarro-Alarc&#243;n

Related Book

First chapter

Adaptive Control Optimization of Cutting Parameters for High Quality Machining Operations Based on Neural Networks and Search Algorithms

By J. V. Abellan, F. Romero, H. R. Siller, A. Estruch and C. Vila

We are IntechOpen, the world's leading publisher of Open Access books. Built by scientists, for scientists. Our readership spans scientists, professors, researchers, librarians, and students, as well as business professionals. We share our knowledge and peer-reveiwed research papers with libraries, scientific and engineering societies, and also work with corporate R&D departments and government entities.

+3,550 Open Access Books

+57,400 Citations in Web of Science

+108,500 IntechOpen Authors and Academic Editors

+560,000 Unique visitors per month

More about us