Robust nonlinear system identification using neural-network models

We study the problem of identification for nonlinear systems in the presence of unknown driving noise, using both feedforward multilayer neural network and radial basis function network models. Our objective is to resolve the difficulty associated with the persistency of excitation condition inherent to the standard schemes in the neural identification literature. This difficulty is circumvented here by a novel formulation and by using a new class of identification algorithms recently obtained by Didinsky et al. (1995). We present a class of identifiers which secure a good approximant for the system nonlinearity provided that some global optimization technique is used. Subsequently, we address the same problem under a third, worst case L/sup /spl infin// criterion for an RBF modeling. We present a neural-network version of an H/sup /spl infin//-based identification algorithm from Didinsky et al., and show how it leads to satisfaction of a relevant persistency of excitation condition, and thereby to robust identification of the nonlinearity.