Radial Basis Function Neural Network Receiver Trained by Kalman Filter Including Evolutionary Techniques

Artificial Neural Networks have been broadly used in several domains of engineering and typical applications involving signal processing. In this paper a channel equalizer using radial basis function neural networks is proposed, on symbol by symbol basis. The radial basis function neural network is trained by an extended Kalman filter including evolutionary techniques. The key motivation for the equalizer application is the neural network capability to establish complex decision regions that are important for estimating the transmitted symbols appropriately. The neural network training process using evolutionary techniques including an extended Kalman filter enables a fast training for the radio basis function neural network. Simulation results are included comparing the proposed method with traditional ones indicating the suitability of the application.