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The problem of equalization for complex signals is presented. A competitive method is proposed for the estimation of the centers of a complex radial basis function neural network. Simulation results are presented from the point of view of mean square error and signal space partition. Concluding remarks and further developments are discussed.
In this paper the problem of equalization of multiple quadrature amplitude modulated signals, using a radial basis function (RBF) neural network, is studied. Because the equalizer performance is directly related to the estimations of the RBF centres, different competitive learning algorithms for the RBF centres are presented. A new competitive algorithm is(More)
This paper offers an overview of complex equalizers, which combine the structure of a linear transversal filter (LTE) with a neural network. There are presented nonlinear channel and nonlinear device models. There are exposed different equalizers architectures and some of the training algorithms. Simulation results are presented and it is made a comparative(More)
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