Channel equalization for ISI channels using Wilcoxon generalized RBF

This paper presents solution to channel equalization problem. Adaptive equalization techniques have been used for channel equalization extensively. Subsequently adaptive signal processing techniques using Artificial Neural Networks based Multilayer Perceptron Network, Radial Basis Function, Recurrent Network, Fuzzy and Adaptive Neuro fuzzy System have been used for these class of problems. In this paper we proposed a RBF equalizer trained with a new neural network learning paradigm called the Wilcoxon learning. This is termed as Wilcoxon generalized radial basis function network (WGRBFN), is a rank based statistics approach, is used to linear regression problems are usually robust against outliers. The Performance of the WGRBFN has been evaluated through extensive computer simulations and the results compared with other equalizers.