Volume 2, Issue 2, June 2009, Pages 124 - 131
Cellular Neural Networks-Based Genetic Algorithm for Optimizing the Behavior of an Unstructured Robot
Alireza Fasih, Jean Chamberlain Chedjou, Kyandoghere Kyamakya
Received 1 October 2008, Revised 19 May 2009, Available Online 1 June 2009.
- https://doi.org/10.2991/ijcis.2009.2.2.3How to use a DOI?
- Cellular Neural Networks, Robot locomotion, Simulation, Genetic Algorithms.
- 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.
- 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 -