Channel equalization using self-constructing fuzzy neural networks with extended Kalman Filter (EKF)

Abstract

In this paper, a self-constructing fuzzy neural networks with extended Kalman filter (SFNNEKF) is proposed. The whole network generalization capability is considered in the hidden neuron growing criterion, which makes the growing process more smoothly. The extended Kalman filter method is used to adjust the free parameters of the fuzzy neural networks. The proposed SFNNEKF learning algorithm is evaluated in channel equalization problems for communication systems. simulation results show that the SFNNEKF equalizer is superior to other equalizers such as recurrent neural network (RNN), minimal resource allocation network (MRAN), the radial basis function neural network (RBFNN) and the growing and pruning RBF (GAP-RBF) network in terms of bit error rate (BER).