Multidimensional Nonlinear Myopic Maps, Volterra Series, and Uniform Neural-Network Approximations

TL;DRAbstract

Our main result is a theorem which gives necessary and sufficient conditions under which discrete-space multidimensional myopic input-output maps with vector-valued inputs drawn from a certain large set can be uniformly approximated arbitrarily well using a structure consisting of a linear preprocessing stage followed by a memoryless nonlinear network. Noncausal as well as causal maps are considered. Approximations for noncausal maps for which inputs and outputs are functions of more than one variable are of current interest in connection with, for example, image processing.

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