A radial basis function framework for various adaptive fuzzy equalizers used in mobile cellular channels

The radial basis function (RBF) based neural networks have been successfully used to solve many non-linear problems, including that of adaptive channel equalization. In this paper, we present three different adaptive fuzzy/neuro-fuzzy channel equalizers that closely fit into the broad framework of RBF neural network based systems. We consider the type-2 fuzzy adaptive filter (FAF) based channel equalizer along with a compensatory neuro-fuzzy filter (CNFF) and the one based on an adaptive network based fuzzy inference system (ANFIS) as applied to mobile cellular channels. We establish that the above three implementations of adaptive equalizers do fit into the generic framework of radial basis function (RBF) based systems.