Neuro-fuzzy identification models

Abstract

The paper deals with the neural net and fuzzy models as universal approximators. Four types of models suitable for identification are presented: the nonlinear output error, the nonlinear input error, the nonlinear generalised output error and the nonlinear generalised input error model. The convergence properties of all four models in the presence of disturbing noise are reviewed and it is shown that the condition for an unbiased identification is that the disturbing noise is white and that it enters the nonlinear model in specific point depending on the type of the model.