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Using a New Model of Recurrent Neural Network for Control

Authors
  • Boquete, L.1
  • Bergasa, L. M.1
  • Barea, R.1
  • García, R.1
  • Mazo, M.1
  • 1 Alcalá University, Electronics Department, 28801, Spain
Type
Published Article
Journal
Neural Processing Letters
Publisher
Kluwer Academic Publishers
Publication Date
Apr 01, 2001
Volume
13
Issue
2
Pages
101–113
Identifiers
DOI: 10.1023/A:1011375420498
Source
Springer Nature
Keywords
License
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Abstract

This paper shows the results obtained in controlling a mobile robot by means of local recurrent neural networks based on a radial basis function (RBF) type architecture. The model used has a Finite Impulse Response (FIR) filter feeding back each neuron's output to its own input, while using another FIR filter as a synaptic connection. The network parameters (coefficients of both filters) are adjusted by means of the gradient descent technique, thus obtaining the stability conditions of the process. As a practical application the system has been successfully used for controlling a wheelchair, using an architecture made up by a neurocontroller and a neuroidentifier. The role of the latter, connected up in parallel with the wheelchair, is to propagate the control error to the neurocontroller, thus cutting down the control error in each working cycle.

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