Continuation of Chaotic Fields by RBFNN

A chaotic field generator is represented by a non-linear equation. Its generating function is modeled empirically by a statistical non-parametric estimator. The estimator corresponds to a radial basis function neural network which learns from a record of a field given in some initial domain to predict the field distribution elsewhere. The performance of the generator is demonstrated by prediction of a chaotic series and a regular as well as a chaotic surface.