Robot Identification using Dynamical Neural Networks

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


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.


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.


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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

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