A dynamic regularized Gaussian radial basis function network for nonlinear, nonstationary time series prediction

A dynamic network of regularized Gaussian radial basis functions (GaRBF) is described for the one-step prediction of nonlinear, nonstationary autoregressive (NLAR) processes governed by a smooth process map and a zero-mean, independent additive disturbance process of bounded variance. For N basis functions, both full-order and reduced-order updating algorithms are introduced, having computational complexities of O (N³) and O (N²), respectively, per time step. Simulations on a 10,000 point, 8-bit quantized 64 k bps rate speech signal show that the proposed dynamic algorithm has a prediction performance comparable and, in some cases, superior to that of AT&T´s LMS-based speech predictor designed for the ITU-T G.721 standard on the 32 kbps ADPCM of speech. The results indicate that the proposed dynamic regularized GaRBF predictor provides a useful tradeoff between its minimal need for prior knowledge of the speech data characteristics and its consequently heavier computational burden