NONLINEAR VECTOR PREDICTION USING FEEDFORWARD NEURAL NETWORKS

The performance of a classical linear vector predictor is limited by i ts ability to exploit only the linear correlation between the blocks. However, a nonlinear predictor exploits the higher order correlations among the neighboring blocks, and can predict edge blocks,vith increas ed accuracy. In this paper, we have investigated several neural networ k architectures that can be used to implement a nonlinear vector predi ctor, including the multilayer perceptron (MLP), the functional link ( FL) network, and the radial basis function (RBF) network. Our experime ntal results show that a neural network predictor can predict the bloc ks containing edges with a higher accuracy than a Linear predictor.