Advertisement

Robot Identification using Dynamical Neural Networks

  • Elias B. Kosmatopoulos
  • Anastasios Chassiakos
  • Manolis A. Christodoulou
Chapter
Part of the Microprocessor-Based and Intelligent Systems Engineering book series (ISCA, volume 9)

Abstract

It is nowadays well known that neural networks can model very efficiently complex nonlinear systems. This paper solves the identification problem of a robotic manipulator using dynamical neural networks. More explicitly a dynamic, distributed backpropagation network with two hidden layers and a novice algorithm are used. The network includes dynamic el-ements in its neurons, and this property makes it effective in identifying dynamic nonlinear systems. Simulation results demonstrate the applicability of the approach.

Keywords

Neural Network Hide Layer Synaptic Weight Robotic Manipulator Dynamical Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    IEEE Transactions on Acoustics Speech and Signal Processing special issue on Neural Networks, vol. 36, no. 7 1988.Google Scholar
  2. [2]
    G. Cybenko, “Approximations by superpositions of a sigmoidal function,”Mathematics of Control Signals and Systemsvol. 2, pp. 183–192, 1989.MathSciNetGoogle Scholar
  3. [3]
    K. Funahashi, “On the approximate realization of continuous mappings by neural net­works,”Neural Networksvol. 2, pp.183–192, 1989.CrossRefGoogle Scholar
  4. [4]
    S. Chen, S. A. Billings, and P. M. Grant, “Non-linear system identification using neural networks,” inInternational Journal of Co ntrolvol. 51, no. 6, pp. 1191–1214, 1990.MathSciNetzbMATHCrossRefGoogle Scholar
  5. [5]
    Fu-C. Chen, “Back-propagation neural networks for nonlinear self-tuning adaptive con­trol,”IEEE Control Systems Magazinevol. 10, no. 3, pp. 44–48, 1990.CrossRefGoogle Scholar
  6. [6]
    D. Rumelhart, D. Hinton, and G. Williams, “Learning internal represantations by error propagation,” in D. Rumelhart and F. McGlelland,edsParallel Distributed Process­ing,Vol. 1. Cambridge, MA:MIT Press, 1986.Google Scholar
  7. [7]
    K. S. Narendra and K. Parthasarathy, “Identification and control of dynamical systems using neural networks,”IEEE Transaction on Neural Networksvol. 1, no. 1, pp. 4–27, March 1990.CrossRefGoogle Scholar
  8. [8]
    J. J. Hopfield, “Neurons with graded response have collective computational properties like those of two-state neurons,”Proc.Natl.Acad.Sci.vol. 81, pp. 3088–3092, 1984.CrossRefGoogle Scholar
  9. [9]
    M. A. Cohen and S. Grossberg, “Masking fields:a massively parallel neural architecture for learning, recognizing, and predicting multiple groupings of patterned data,”Applied Opticsvol. 16, no. l0,pp. 1866–1890.Google Scholar
  10. [10]
    M. A. Cohen and S. Grossberg, “Absolute stability of global pattern formation and parallel memory storage by competitive neural networks, ” IEEE Trans., Sy-st., Man, Cyber., vol. SMC- 13, pp. 815–826.. Sept./Oct. 1983.MathSciNetCrossRefGoogle Scholar
  11. [11]
    J. J. Craig, Introduction to Robotics. Mechanics and Control, second edition, Addison-Wesley Pub. Co., 1989.Google Scholar
  12. [12]
    A. Chassiakos, “Distributed control of robot manipulators,” PhD thesis, University of Southern California, LA, CA, 1984.Google Scholar
  13. [13]
    P. D. WassermanNeural Computing. Theory and PracticeVan Nostrant Reinhold, New York, 1989.Google Scholar
  14. [14]
    S. A. Elias and S. Grossberg, “Pattern formation, Contrast control, and oscillations in the short term memory of shuting on-center off-surround networks,”Biol. Cybcrnct.vol. 20,p. 69, 1975.CrossRefGoogle Scholar
  15. [15]
    G. A. Carpenter, “A geometrical approach to singular perturbation problems with ap­plication to nerve impulse equations,”J. Differential Equationsvol 23, p. 335, 1977MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1991

Authors and Affiliations

  • Elias B. Kosmatopoulos
    • 1
  • Anastasios Chassiakos
    • 2
  • Manolis A. Christodoulou
    • 1
  1. 1.Department of Electronic and Computer EngineeringTechnical University of CreteChania,CreteGreece
  2. 2.California State University, School of Engineering-EIT, Long BeachUSA

Personalised recommendations