A novel fast Kolmogorov's spline complex network for pattern detection
WSEAS TRANSACTIONS on SYSTEMS
A novel fast Kolmogorov's spline complex network for pattern detection
SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
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In this article a new neural-network architecture suitable for learning and generalization is discussed and developed. Although similar to the radial basis function (RBF) net, our computational model called the net with complex weights (CWN) has demonstrated a considerable gain in performance and efficiency in number of applications compared to RBF net. Its better performance in classification tasks is explained by the cross-product terms in internal representation of its basis function introduced parsimoniously. Implementation of CWN by the ensemble approach is described. A number of examples, solved using CWN and other networks, are used to illustrate the desirable characteristics of CWN