Probabilistic Interval Predictor Based on Dissimilarity Functions

• 成果类型: Article
• 署名作者: Carnerero, A. D.; Ramirez, D. R.; Alamo, T.
• 署名单位: University of Sevilla
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3136137
• 发表日期: 2022
• 页码: 6842-6849
• 关键词: Nonlinear systems; prediction intervals; System identification; uncertainty

摘要

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.