Maximum likelihood identification of Wiener models with a linear regression initialization

Many parametric identification routines suffer from the problem with local minima. This is also true for the prediction-error approach to identifying Wiener models, i.e., linear models with a static nonlinearity at the output. We here suggest a linear regression initialization that secures a consistent and efficient estimate, when used in conjunction with a Gauss-Newton minimization scheme