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system identification toolbox constructs linear and nonlinear models of physical systems from measured time and frequency domain data you can create models of dynamic systems that are not easily modeled from first principles or specifications you can estimate many types of linear parametric models for example you can estimate continuous time transfer functions where you can specify the number of poles and zeros and the tool will determine numerator and denominator coefficients as well as a time delay automatically you can estimate States based models process models and common polynomial models such as air racks our max output error and box rankings for some of these model types system identification toolbox can help you select model order to achieve the optimal trade-off between model fidelity and model complexity you can also create nonlinear models such as nonlinear Erics and Hammerstein linear models in these models you can capture realistic effects such as static friction in a mo