Radial basis function networks with FIR/IIR synapses
Neural Processing Letters
On the Design and Implementation of a Neuromorphic Self-Tuning Controller
Neural Processing Letters
Neural Control of the Movements of a Wheelchair
Journal of Intelligent and Robotic Systems
Learning rules for neuro-controller via simultaneous perturbation
IEEE Transactions on Neural Networks
A direct adaptive neural-network control for unknown nonlinear systems and its application
IEEE Transactions on Neural Networks
Online stabilization of block-diagonal recurrent neural networks
IEEE Transactions on Neural Networks
On-line learning algorithms for locally recurrent neural networks
IEEE Transactions on Neural Networks
Diagonal recurrent neural networks for dynamic systems control
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
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.