Probabilistic interval predictor based on dissimilarity functions
| Autor: | Alfonso Daniel Carnerero Panduro, Teodoro Rafael Alamo Cantarero, Daniel Rodríguez Ramírez |
|---|---|
| Přispěvatelé: | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática, Universidad de Sevilla. TEP950: Estimación, Predicción, Optimización y Control |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Uncertainty
Mathematics - Statistics Theory Systems and Control (eess.SY) Statistics Theory (math.ST) Electrical Engineering and Systems Science - Systems and Control Computer Science Applications Bounded noise Control and Systems Engineering Nonlinear systems FOS: Electrical engineering electronic engineering information engineering FOS: Mathematics Electrical and Electronic Engineering System identification Prediction intervals |
| Zdroj: | idUS. Depósito de Investigación de la Universidad de Sevilla instname |
| Popis: | 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. 10 pages, 3 figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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