System identification: theory for the user
System identification: theory for the user
Algorithms in combinatorial geometry
Algorithms in combinatorial geometry
Information-based complexity
Structure identification of nonlinear dynamic systems—a survey on input/output approaches
Automatica (Journal of IFAC)
Optimal estimation theory for dynamic systems with set membership uncertainty: an overview
Automatica (Journal of IFAC)
Perturbation signals for system identification
Perturbation signals for system identification
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Bounding Approaches to System Identification
Bounding Approaches to System Identification
Open problems for tractability of multivariate integration
Journal of Complexity
Parametric and nonparametric curve fitting
Automatica (Journal of IFAC)
From experiment design to closed-loop control
Automatica (Journal of IFAC)
Set Membership identification of nonlinear systems
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
Unified Set Membership theory for identification, prediction and filtering of nonlinear systems
Automatica (Journal of IFAC)
A combined Moving Horizon and Direct Virtual Sensor approach for constrained nonlinear estimation
Automatica (Journal of IFAC)
Direct feedback control design for nonlinear systems
Automatica (Journal of IFAC)
| Hi-index | 0.01 |
System identification consists in finding a model of an unknown system starting from a finite set of noise-corrupted data. A fundamental problem in this context is to asses the accuracy of the identified model. In this paper, the problem is investigated for the case of nonlinear systems within the Set Membership-Information Based Complexity framework of [M. Milanese, C. Novara, Set membership identification of nonlinear systems, Automatica 40(6) (2004) 957-975]. In that paper, a (locally) optimal algorithm has been derived, giving (locally) optimal models in nonlinear regression form. The corresponding (local) radius of information, providing the worst-case identification error, can be consequently used to measure the quality of the identified model. In the present paper, two algorithms are proposed for the computation of the local radius of information: The first provides the exact value but requires a computational complexity exponential in the dimension of the regressor space. The second is approximate but involves a polynomial (quadratic) complexity.