Regularization aspects in continuous-time model identification (Q1764037)

This paper deals with an ill-posed inverse problem for which the Tikhonov regularization is used. The principle of continuous-time model identification by the linear filters method is presented as well as its relation with regularization techniques. To do that the authors formalize the regularization properties of linear filters. A new structure of filters is proposed and the continuous-time model identification problem is formulated as a joint system input-output signal and model parameter estimation. A simulation example is considered.