A net with complex weights

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.