用区间分析法验证非线性估计器的误差上限，确保决策安全。
Interval-based validation of a nonlinear estimator
- 基于Moore-Skelboe算法，通过区间运算保证估计误差不超限。
- 可为神经网络等非保证型估计器提供最大误差的严格上界。
- 适合对可靠性要求高的工程系统，如自动驾驶、工业控制。
在工程中，模型常用于描述系统行为，需通过估计器根据观测数据逼近模型参数。这种逼近会引入预测值与实际观测之间的差异，带来不确定性，可能导致危险决策。区间分析工具可用于在不依赖区间分析的估计器（如神经网络）基础上，保证其某些性质。本文提出一种基于区间的、可保证的非线性估计器验证方法，基于Moore-Skelboe算法，能够返回估计器永远不会超过的保证最大误差。结果表明，即使对非保证型估计器，也能实现误差边界的确保。
原文摘要 · Abstract (English)
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.
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