Recursive membership estimation for output-error models
Mathematics and Computers in Simulation - Parameter identifications with error bound
Estimation theory for nonlinear models and set membership uncertainty
Automatica (Journal of IFAC)
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.)
C++ Toolbox for Verified Computing I: Basic Numerical Problems Theory, Algorithms, and Programs
C++ Toolbox for Verified Computing I: Basic Numerical Problems Theory, Algorithms, and Programs
Automatica (Journal of IFAC)
Artificial Intelligence
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In the context of bounded-error estimation, it is customary to assume that the error between the model output and output data should lie between some known prior bounds. In this paper, it is also assumed that the factors characterizing the experiments that have been carried out (e.g., measurement times) are uncertain, with known prior bounds. An algorithm based on interval analysis is used to characterize the set of all values of the parameter vector to be estimated that are consistent with these hypotheses. This is performed in a guaranteed way, even when the model output is a nonlinear function of the parameters and factors characterizing the experiments.