4. Identification method of nonlinear parameter varying models (NPV) as claimed in claim 3 wherein the system identification method based on Hammerstein structural non-parametric/parametric hybrid model in said step (2) adopts the nonlinear hybrid Hammerstein model structure, said structure is consisting of the static normalized direct linear feedback neural networks (DLF) part and the linear part of the model.
3. Identification method of nonlinear parameter varying models (NPV) as claimed in claim 2 wherein the system identification method based on Hammerstein or Wiener structural model in said step (2) is the system identification method based on Hammerstein or Wiener structural parametric model, or is the system identification method based on Hammerstein or Wiener structural non-parametric/parametric hybrid model, the Hammerstein or Wiener structural model is consisting of the normalized static nonlinear part of the model and the linear part of the model.