用于滤波的f框

O. Strauss, Sébastien Destercke
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引用次数: 2

摘要

当滤波数字信号时,选择一个特定的求和核(即,形式上等同于概率分布)可能是一项困难的任务。这个问题的一个明显的解决方案是使用多个内核而不是单个内核进行过滤,但是这种解决方案的计算成本很快就会变得令人望而却步(特别是在实时应用程序中)。另一种选择,即本文所研究的,是考虑由不精确概率表示建模的核。考虑到这样的表示使得使用来自不精确概率论的数值工具成为可能,例如Choquet积分,并且允许一个人在不增加太多所需计算数量的情况下处理多个核。在本文中,我们建议使用众所周知的p-box表示来滤波数字信号。我们表明,使用p-box可以做出比可能性分布和云更精确的推断。然后我们讨论了用pboxes计算滤波信号的实际方面,并通过一些实验来结束。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
F-boxes for filtering
Selecting a particular summative kernel (i.e., formally equivalent to a probability distribution) when filtering a digital signal can be a difficult task. An obvious solution to this problem is to filter with multiple kernels rather than with a single one, but the computing cost of such a solution can quickly become prohibitive (especially in real-time applications). Another alternative, the one studied in this paper, is to consider kernels modeled by imprecise probabilistic representations. Considering such representations makes the use of numerical tools coming from imprecise probability theory possible, such as the Choquet integral, and allows one to work with multiple kernels without multiplying too much the number of required computations. In this paper, we propose to use the well-known p-box representation to filter a digital signal. We show that the use p-boxes allows making more precise inferences than those obtained with possibility distributions and clouds. We then discuss the practical aspect of computing a filtered signal with pboxes, and finish by some experiments.
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