A vector predictor is an integral part of the predictive vector quantization (PVQ) scheme. The performance of a predictor deteriorates as the vector dimension (block size) is increased. This makes it necessary to investigate new design techniques in order to design a vector predictor which gives better performance when compared to a conventional vector predictor. This paper investigates several neural network configurations which can be employed in order to design a vector predictor. The following architectures are investigated: (a) multilayer perceptron, (b) functional link network, and (c) radial basis function network. The performance of the above mentioned neural network vector predictors is evaluated and compared with that of a linear vector predictor.
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