Strongly optimal algorithms and optimal information in estimation problems
Journal of Complexity
Constraint propagation with interval labels
Artificial Intelligence
Estimation theory for nonlinear models and set membership uncertainty
Automatica (Journal of IFAC)
Parameter estimation algorithms for a set-membership description of uncertainty
Automatica (Journal of IFAC)
Constraint reasoning based on interval arithmetic: the tolerance propagation approach
Artificial Intelligence - Special volume on constraint-based reasoning
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
On the value of information in system identification-Bounded noise case
Automatica (Journal of IFAC)
Nonlinear bounded-error parameter estimation using interval computation
Granular computing
Numerical solution for bounding feasible point sets
Journal of Computational and Applied Mathematics
Automatica (Journal of IFAC)
Set-theoretic estimation of hybrid system configurations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Constraint propagation on quadratic constraints
Constraints
Making adaptive an interval constraint propagation algorithm exploiting monotonicity
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
A box-consistency contractor based on extremal functions
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
COCOS'03 Proceedings of the Second international conference on Global Optimization and Constraint Satisfaction
| Hi-index | 22.15 |
For a large class of bounded-error estimation problems, the posterior feasible set S for the parameters can be defined by nonlinear inequalities. The set inversion approach combines classical interval analysis with branch-and-bound algorithms to characterize S. Unfortunately, as bisections have to be done in all directions of the parameter space, this approach is limited to problems involving a small number of parameters. Techniques based on interval constraint propagation make it possible to drastically reduce the number of bisections. In this paper, these techniques are combined with set inversion to bracket S between inner and outer subpavings (union of nonoverlapping boxes). When only interested in the feasible intervals for the parameters, the set inversion approach becomes inefficient, and a new algorithm able to compute these intervals is given. This algorithm uses a new interval-based local research to compute the smallest box that contains S. It is then compared with existing methods on an example taken from the literature.