Data reconciliation consists in modifying noisy or unreliable data in order to satisfy a mathematical model (herein a material flow network). The conventional approach relies on least squares minimization. Here we show that the setting of fuzzy sets provides a generalized approach that is more flexible and less dependent on oftentimes debatable probabilistic justifications. Moreover the proposed setting also encompasses constraint-based formulations using intervals.
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