In engineering, models are often used to represent the behavior of a system. Estimators are then needed to approximate the values of the model's parameters based on observations. This approximation implies a difference between the values predicted by the model and the observations that have been made. It creates an uncertainty that can lead to dangerous decision making. Interval analysis tools can be used to guarantee some properties of an estimator, even when the estimator itself doesn't rely on interval analysis (Adam, 2019) (Adam, 2015). This paper contributes to this dynamic by proposing an interval-based and guaranteed method to validate a nonlinear estimator. It is based on the Moore-Skelboe algorithm (van Emden, 2004). This method returns a guaranteed maximum error that the estimator will never exceed. We will show that we can guarantee properties even when working with non-guaranteed estimators such as neural networks.

翻译：在工程领域，模型常被用于描述系统行为。随后需要基于观测数据，通过估计器来近似模型参数值。这种近似意味着模型预测值与实际观测值之间存在差异，由此产生的不确定性可能导致危险的决策失误。区间分析工具可用于保证估计器的某些性质，即使估计器本身不依赖于区间分析（Adam, 2019）（Adam, 2015）。本文通过提出一种基于区间分析的确定性验证方法，为非线性估计器的验证研究作出贡献。该方法基于Moore-Skelboe算法（van Emden, 2004），能够给出估计器永远不会超过的确定性最大误差界。我们将证明，即使对于神经网络这类非确定性估计器，该方法仍能保证其性质验证的可靠性。