Application of Bayesian trained RBF networks to nonlinear time-series modeling

摘要

We examine Bayesian learning of a regularization factor and the noise level of radial basis function (RBF) networks in the framework of nonlinear time-series prediction and system modeling. A Bayesian trained RBF network is applied in an autonomous recursive prediction model (oscillator model) for regenerating time-series generated by the Lorenz system and speech signals. The oscillator model is able to capture the invariant measures of the Lorenz system for high enough SNR, and to reproduce the voiced part of speech signals.