Fully complex-valued radial basis function networks for orthogonal least squares regression

— We consider a fully complex-valued radial basis function (RBF) network for regression application. The locally regularised orthogonal least squares (LROLS) algorithm with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF network models, is extended to the fully complex-valued RBF network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the Doptimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression applic...

• Type: Conference
• Year: 2008
• Where: IJCNN
• Authors: Sheng Chen, Xia Hong, Chris J. Harris
• Keywords: Artificial Intelligence, Complex-valued Rbf Network, IJCNN 2008, Parsimonious Real-valued Rbf, RBF Network