This paper deals with the identification of FIR models corrupted by white input noise and colored output noise. An identification algorithm that exploits the properties of both the dynamic Frisch scheme and the high-order Yule-Walker (HOYW) equations is proposed. It is shown how the HOYW equations allow to define a selection criterion for identifying the input noise variance (and then the FIR coefficients) within the Frisch locus of solutions. The proposed approach does not require any a priori knowledge about the input and output noise variances. The algorithm performance is assessed by means of Monte Carlo simulations
This paper deals with the identification of errors–in–variables models where the additive input and ...
none3noThis paper deals with the identification of errors–in–variables models where the additive inp...
This paper describes a new approach for identifying FIR models from a finite number of measurements,...
This paper deals with the identification of FIR models corrupted by white input noise and colored o...
This paper proposes an efficient algorithm for identifying FIR models when also the input is assumed...
This paper proposes an efficient algorithm for identifying FIR models when also the input is assumed...
This paper describes a method for identifying FIR models in the presence of input and output noise. ...
A new method for identifying linear dynamic errors-in-variables (EIV) models, whose input and output...
A new method for identifying linear dynamic errors-in-variables (EIV) models, whose input and output...
This paper describes a method for identifying FIR models in the presence of input and output noise. ...
This paper describes a new approach for identifying FIR models from a finite number of measurements...
This paper describes a new approach for identifying FIR models from a finite number of measurements,...
This paper describes a new approach for identifying FIR models from a finite number of measurements...
This paper deals with the identification of errors–in–variables models where the additive input and ...
none3A new method for identifying linear dynamic errors-in-variables (EIV) models, whose input and o...
This paper deals with the identification of errors–in–variables models where the additive input and ...
none3noThis paper deals with the identification of errors–in–variables models where the additive inp...
This paper describes a new approach for identifying FIR models from a finite number of measurements,...
This paper deals with the identification of FIR models corrupted by white input noise and colored o...
This paper proposes an efficient algorithm for identifying FIR models when also the input is assumed...
This paper proposes an efficient algorithm for identifying FIR models when also the input is assumed...
This paper describes a method for identifying FIR models in the presence of input and output noise. ...
A new method for identifying linear dynamic errors-in-variables (EIV) models, whose input and output...
A new method for identifying linear dynamic errors-in-variables (EIV) models, whose input and output...
This paper describes a method for identifying FIR models in the presence of input and output noise. ...
This paper describes a new approach for identifying FIR models from a finite number of measurements...
This paper describes a new approach for identifying FIR models from a finite number of measurements,...
This paper describes a new approach for identifying FIR models from a finite number of measurements...
This paper deals with the identification of errors–in–variables models where the additive input and ...
none3A new method for identifying linear dynamic errors-in-variables (EIV) models, whose input and o...
This paper deals with the identification of errors–in–variables models where the additive input and ...
none3noThis paper deals with the identification of errors–in–variables models where the additive inp...
This paper describes a new approach for identifying FIR models from a finite number of measurements,...