The paper proposes an inventive method for nonlinear system identification.

The approach integrates three novel modeling and identification strategies.

It uses NARMAX models, set-based parameter identification, and evolutionary algorithms.

This method addresses both model structure selection and parameter estimation.

The method operates in different noise scenarios, especially correlated noise.