The existing results show the applicability of the Over-Parameterized Model based Hammerstein-Wiener model identification methods. However, it requires to estimate extra parameters and performer a low rank approximation step. Therefore, it may give rise to unnecessarily high variance in parameter estimates for highly nonlinear systems, especially using a small and noisy data set. To overcome this corruptive phenomenon. To overcome this corruptive phenomenon, in this paper, a robust Hammerstein-Wiener model identification method is developed for highly nonlinear systems when using a small and noisy data set, where two parsimonious parametrization models with fewer parameters are used, and an iteration method is then used to retrieve the true system parameters from the parametrization models. Such modification can improve the parameter estimation performance in terms of accuracy and variance compared with the over-parametrization model based identification methods. All the above-mentioned developments are analyzed with variance analysis, along with a simulation example to confirm the effectiveness.