This paper proposes an L-two-optimal identification approach to cope with errors-in-variables model （EIVM） identification. With normalized coprime factor model （NCFM） representations, L-two-optimal approximate mod...This paper proposes an L-two-optimal identification approach to cope with errors-in-variables model （EIVM） identification. With normalized coprime factor model （NCFM） representations, L-two-optimal approximate models are derived from the framework of an EIVM according to the kernel and image representations of related signals. Based on the optimal approximate models, the v-gap metric is employed as a minimization criterion to optimize the parameters of a system model, and thus the resulting optimization problem can be solved by linear matrix inequalities （LMIs）. In terms of the optimized system model, the noise model （NM） can be readily obtained by right multiplication of an inner. Compared with other EIVM identification methods, the proposed one has a wider scope of applications because the statistical properties of disturbing noises are not demanded. It is also capable of giving identifiabiUty. Finally, a numerical simulation is used to verify the effectiveness of the proposed method.展开更多
文摘This paper proposes an L-two-optimal identification approach to cope with errors-in-variables model （EIVM） identification. With normalized coprime factor model （NCFM） representations, L-two-optimal approximate models are derived from the framework of an EIVM according to the kernel and image representations of related signals. Based on the optimal approximate models, the v-gap metric is employed as a minimization criterion to optimize the parameters of a system model, and thus the resulting optimization problem can be solved by linear matrix inequalities （LMIs）. In terms of the optimized system model, the noise model （NM） can be readily obtained by right multiplication of an inner. Compared with other EIVM identification methods, the proposed one has a wider scope of applications because the statistical properties of disturbing noises are not demanded. It is also capable of giving identifiabiUty. Finally, a numerical simulation is used to verify the effectiveness of the proposed method.
