论文标题
基于差异函数的概率间隔预测变量
Probabilistic interval predictor based on dissimilarity functions
论文作者
论文摘要
这项工作提出了一种新的方法，以获得动态系统的概率间隔预测。提出的策略使用存储的过去系统测量来估计系统的未来演变。该方法依赖于使用差异函数来估计输出的条件概率密度函数。引入了一个经验概率密度函数的家族，通过两个标量函数进行了参数。结果表明，所提出的家庭包含多变量的正常概率密度作为特定情况。我们表明，提出的方法构成了经典估计方法的概括。验证方案用于调整该方法依赖的两个参数。为了证明提出的方法的有效性，提供了一些数值示例和比较。
This work presents a new methodology to obtain probabilistic interval predictions of a dynamical system. The proposed strategy uses stored past system measurements to estimate the future evolution of the system. The method relies on the use of dissimilarity functions to estimate the conditional probability density function of the outputs. A family of empirical probability density functions, parameterized by means of two scalars, is introduced. It is shown that the proposed family encompasses the multivariable normal probability density function as a particular case. We show that the presented approach constitutes a generalization of classical estimation methods. A validation scheme is used to tune the two parameters on which the methodology relies. In order to prove the effectiveness of the presented methodology, some numerical examples and comparisons are provided.
