Analysis of autoassociative mapping neural networks

In this paper we analyse the mapping behavior of an autoassociative neural network (AANN). The mapping in an AANN is achieved by using a dimension reduction followed by a dimension expansion. One of the major results of the analysis is that, the network performs better autoassociation as the size increases. This is because, a network of a given size can deal with only a certain level of nonlinearity. Performance of autoassociative mapping is illustrated with 2D examples. We have shown the utility of the mapping feature of an AANN for speaker verification