Robust regularized learning using distributed approximating functional networks

Abstract

We present a novel polynomial functional neural networks using distributed approximating functional (DAF) wavelets (infinitely smooth filters in both time and frequency regimes), for signal estimation and surface fitting. The remarkable advantage of these polynomial nets is that the functional space smoothness is identical to the state space smoothness (consisting of the weighting vectors). The constrained cost energy function using optimal regularization programming endows the networks with a natural time-varying filtering feature. Theoretical analysis and an application show that the approach is extremely stable and efficient for signal processing and curve/surface fitting.