International Journal of Computational Intelligence Systems

Volume 2, Issue 2, June 2009, Pages 124 - 131

Cellular Neural Networks-Based Genetic Algorithm for Optimizing the Behavior of an Unstructured Robot

Authors
Alireza Fasih, Jean Chamberlain Chedjou, Kyandoghere Kyamakya
Corresponding Author
Alireza Fasih
Available Online 16 June 2009.
DOI
https://doi.org/10.2991/ijcis.2009.2.2.3How to use a DOI?
Keywords
Cellular Neural Networks, Robot locomotion, Simulation, Genetic Algorithms.
Abstract
A new learning algorithm for advanced robot locomotion is presented in this paper. This method involves both Cellular Neural Networks (CNN) technology and an evolutionary process based on genetic algorithm (GA) for a learning process. Learning is formulated as an optimization problem. CNN Templates are derived by GA after an optimization process. Through these templates the CNN computation platform generates a specific wave leading to the best motion of a walker robot. It is demonstrated that due to the new method presented in this paper an irregular and even a disjointed walker robot can successfully move with the highest performance.
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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
2 - 2
Pages
124 - 131
Publication Date
2009/06
ISSN
1875-6883
DOI
https://doi.org/10.2991/ijcis.2009.2.2.3How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Alireza Fasih
AU  - Jean Chamberlain Chedjou
AU  - Kyandoghere Kyamakya
PY  - 2009
DA  - 2009/06
TI  - Cellular Neural Networks-Based Genetic Algorithm for Optimizing the Behavior of an Unstructured Robot
JO  - International Journal of Computational Intelligence Systems
SP  - 124
EP  - 131
VL  - 2
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2009.2.2.3
DO  - https://doi.org/10.2991/ijcis.2009.2.2.3
ID  - Fasih2009
ER  -