Using a New Model of Recurrent Neural Network for Control

Boquete, Luciano; Bergasa, Luis M.; Barea, Rafael; García, Ricardo; Mazo, M.
Year: 2001
Type of Publication: Article
Journal: Neural Processing Letters
Volume: 13
Pages: 101-113
ISSN: 1370-4621
Note: 10.1023/A:1011375420498
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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