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Model selection via worst-case criterion for nonlinear bounded-error estimation
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DOI：10.1109/19.850410.png)
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摘要
En 中文
In this paper, the problem of model selection for measurement purpose is studied, A new selection procedure in a deterministic framework is proposed. The problem of nonlinear bounded-error estimation is viewed as a set inversion procedure. As each candidate model structure leads to a specific set of admissible values of the measurement vector, the worst-ease criterion is used to select the optimal model. The selection procedure is applied to a real measurement problem, grooves dimensioning using remote field eddy current (RFEC) inspection.
Keyword:
interval analysis
inverse problems
model selection
nonlinear estimation
worst-case design
期刊
IF:
5.9
论文数:
2.0W
被引数:
5.8W
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