区间直线拟合
Interval straight line fitting
AI总结：
本文提出一种基于约束满足的区间直线拟合方法，通过盒切片算法获得解的区间包络，无需最小化泛函和加权数据，可处理变量不确定性并适用于异常值检测等任务。
摘要：
我考虑了实验数据拟合的任务。与传统方法不同，我并不试图基于可用的实验信息最小化任何泛函，而是将最小化问题替换为约束满足过程，该过程产生所需类型解的区间包络。文中详细描述了称为“盒切片算法”的方法。通过这种方式获得的结果无需标注任何类型的置信水平，它们只是确定的（有保证的）。该方法轻松处理一个或两个变量中存在不确定性的情况。无需对实验数据进行总是或多或少任意的加权。该方法可直接应用于其他实验数据处理问题，如异常值检测或寻找与实验曲线相切的直线。
英文摘要：
I consider the task of experimental data fitting. Unlike the traditional approach I do not try to minimize any functional based on available experimental information, instead the minimization problem is replaced with constraint satisfaction procedure, which produces the interval hull of solutions of desired type. The method, called 'box slicing algorithm', is described in details. The results obtained this way need not to be labeled with confidence level of any kind, they are simply certain (guaranteed). The method easily handles the case with uncertainties in one or both variables. There is no need for, always more or less arbitrary, weighting the experimental data. The approach is directly applicable to other experimental data processing problems like outliers detection or finding the straight line, which is tangent to the experimental curve.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
