On-line Stable Nonlinear Modelling by Structurally Adaptive Neural Nets

This paper proposes a neural net based on-line scheme for modelling discrete-time mul-tivariable nonlinear dynamical systems. Taking the advantage of structural features of RBF (Radial-Basis-Function) neural nets, the method approaches the modelling problem by setting up a coarse RBF model structure in the light of the spatial Fourier transform and spatial sampling theory, then devising appropriate on-line algorithms to carry out reenements for both the RBF net structure and the associated weights. Main convergence results are established in the paper along with the analysis backing up the structure initialization and adaptation. The eeectiveness of the scheme is illustrated with an simulation example.