Self-organizing Radial Basis Function Network Modeling for Robot Manipulator (original) (raw)

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.

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Authors and Affiliations

  1. Department of Electrical Engineering, Korea University, 1, 5-Ka Anam-Dong, Seongbuk-Gu, Seoul, 136-701, Korea
    Dongwon Kim, Sung-Hoe Huh & Gwi-Tae Park
  2. Department of Electrical & Electronic Engineering, Anyang University, 708-113, Anyang 5dong, Manan-gu, Anyang-shi, Kyunggi-do, 430-714, Korea
    Sam-Jun Seo

Authors

  1. Dongwon Kim
  2. Sung-Hoe Huh
  3. Sam-Jun Seo
  4. Gwi-Tae Park

Editor information

Editors and Affiliations

  1. Department of Computer Science, Texas State University-San Marcos, Nueces 247, 601 University Drive, 78666-4616, San Marcos, TX, USA
    Moonis Ali
  2. Dipartimento di Informatica, Università degli Studi di Bari,
    Floriana Esposito

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© 2005 Springer-Verlag Berlin Heidelberg

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Kim, D., Huh, SH., Seo, SJ., Park, GT. (2005). Self-organizing Radial Basis Function Network Modeling for Robot Manipulator. In: Ali, M., Esposito, F. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2005. Lecture Notes in Computer Science(), vol 3533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504894\_81

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