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- W2004348444 abstract "The design of Fault Detection and Isolation (FDI) systems is generally based on models identified by minimizing adjustment error. For non-linear processes, the use of observers based on Takagi-Sugeno fuzzy models have been proposed. However, from a detection and isolation point of view, models obtained using known identification algorithms are not necessarily optimal. This is showed through evaluations made using a benchmark hydraulic system simulator, with a confusion matrix, mean detection time and mean time between false alarms as performance indicators. Based on these results, we propose a new design procedure employing multiobjective optimization that improves on the above-mentioned time indicators by a factor varying between 1.5 and 3.7." @default.
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- W2004348444 date "2006-01-01" @default.
- W2004348444 modified "2023-09-24" @default.
- W2004348444 title "OPTIMAL IDENTIFICATION OF TAKAGI-SUGENO FUZZY MODELS FOR NONLINEAR FDI" @default.
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- W2004348444 doi "https://doi.org/10.3182/20060829-4-cn-2909.00126" @default.
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