Constraint propagation with interval labels
Artificial Intelligence
Set inversion via interval analysis for nonlinear bounded-error estimation
Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
Guaranteed nonlinear parameter estimation from bounded-error data via interval analysis
Mathematics and Computers in Simulation
Methods and Applications of Interval Analysis (SIAM Studies in Applied and Numerical Mathematics) (Siam Studies in Applied Mathematics, 2.)
Technical Communique: Interval constraint propagation with application to bounded-error estimation
Automatica (Journal of IFAC)
| Hi-index | 0.00 |
This paper deals with the estimation of the parameters of a model from experimental data. The aim of the method presented is to characterize the set S of all values of the parameter vector that are acceptable in the sense that all errors between the experimental data and the corresponding model outputs lie between known lower and upper bounds. This corresponds to what is known as bounded-error estimation or membership-set estimation. Most of the methods available to give guaranteed estimates of S rely on the hypothesis that the model output is linear in its parameters, contrary to the method advocated here which can deal with nonlinear models. This is made possible by the use of the tools of interval analysis, combined with a branch-and-bound algorithm. The purpose of the present paper is to show that the approach can be cast into the more general framework of granular computing.