Title: Environment-adaptation mobile radio propagation prediction using radial basis function neural networks
Authors: Chang, PR
Yang, WH
National Chiao Tung University
Institute of Communications Engineering
Keywords: propagation prediction;RBF neural networks
Issue Date: 1-Feb-1997
Abstract: This paper investigates the application of a radial basis function (RBF) neural network to the prediction of Field strength based on topographical and morphographical data, The RBF neural network Is a two-layer localized receptive field network whose output nodes from a combination of radial activation functions computed by the hidden layer nodes. Appropriate centers and connection weights in the RBF network lead to a network that is capable of forming the best approximation to any continuous nonlinear mapping up to an arbitrary resolution. Such an approximation introduces best nonlinear approximation capability into the prediction model in order to accurately predict propagation loss over an arbitrary environment based on adaptive learning from measurement data, The adaptive learning employs hybrid competitive and recursive least squares algorithms, The unsupervised competitive algorithm adjusts the centers while the recursive least squares (RLS) algorithm estimates the connection weights. Because these two learning rules are both linear, rapid convergence is guaranteed. This hybrid algorithm significantly enhances the real-time or adaptive capability of the RBF-based prediction model, The applications to Okumura's data are included to demonstrate the effectiveness of the RBF neural network approach.
URI: http://dx.doi.org/10.1109/25.554747
ISSN: 0018-9545
DOI: 10.1109/25.554747
Volume: 46
Issue: 1
Begin Page: 155
End Page: 160
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  1. A1997WJ49200016.pdf