Spectral analysis and synthesis of three-layered feed-forward neural networks for function approximation

Abstract

The universal approximation capability exhibited by one-hidden-layer neural network is explored to create a new synthesis method for minimized architectures suited for VLSI implementation. The development is based on the spectral analysis of the network, which focuses their capability of combining single neurons spectra to obtain the spectrum of the function to approximate. In this paper, we propose a new spectrum-based technique to synthesize 1-N-1 networks which approximate y=f(x) functions, with x, y/spl isin/R.