Título: System Identification through RBF Neural Networks: Improving Accuracy by a Numerical Approximation Method for the Centroids and Widths Adjustment
Autores: Oliveira, Paulo;Braga, Arthur
Within the last two decades there has been an increasing need for the development of mathematical models out of observed data captured from a system, a process called empirical modelling or systems identification. Under this circumstance, many techniques and methodologies have been proposed, among them the use of Artificial Neural Networks. It is proposed herein a non-hybrid gradient-based learning algorithm for a Radial Basis Function Neural Network aimed at improving the accuracy of non-linear dynamical system modelling. A single-stage non-hybrid approach is employed for the learning process, where the free parameters of the network – the centroids positioning, the receptive fields width, and the weights – are updated through a supervised method. Accurate identification capability is examined by the use of two non-linear datasets and the performance of the proposed method is compared with traditional techniques. Results demonstrate that nonlinear system identification can be significantly improved with easy-toimplement gradient-based RBF learning strategy
Artificial Neural Networks; System Identification; RBF; Parameter Estimation; Gradient Descent; Supervised Learning.
Código DOI: 10.21528/CBIC2017-132
Artigo em pdf: cbic-paper-132.pdf
Arquivo BibTeX: cbic-paper-132.bib