Self-organizing radial basis function network modeling for robot manipulator

Authors:
Dongwon Kim;Sung-Hoe Huh;Sam-Jun Seo;Gwi-Tae Park
Affiliations:
Department of Electrical Engineering, Korea University, Seongbuk-Gu, Seoul, Korea;Department of Electrical Engineering, Korea University, Seongbuk-Gu, Seoul, Korea;Department of Electrical & Electronic Engineering, Anyang University, Manan-gu, Anyang-shi, Kyunggi-do, Korea;Department of Electrical Engineering, Korea University, Anam-Dong, Seongbuk-Gu, Seoul, Korea
Venue:
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Year:
2005

Citing 4
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Abstract

Intelligent and adaptive approach to model two links manipulator system with self-organizing radial basis function (RBF) network is presented in this paper. The self-organizing algorithm that enables the RBF neural network to be structured automatically and on-line is developed, and with this proposed scheme, the centers and widths of RBF neural network as well as the weights are to be adaptively determined. Based on the fact that a 3-layered RBF neural network has the capability that represents the nonlinear input-output map of any nonlinear function to a desired accuracy, the input output mapping of the two link manipulator using the proposed RBF neural network is shown analytically through experimental results without knowing the information of the system in advance.