Review on Fuzzy and Neural Prediction Interval Modelling for Nonlinear Dynamical Systems
The existing uncertainties during the operation of processes could strongly affect the performance of forecasting systems, control strategies and fault detection systems when they are not considered in the design. Because of that, the study of uncertainty quantification has gained more attention amo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9343299/ |
| _version_ | 1818351248364011520 |
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| author | Oscar Cartagena Sebastian Parra Diego Munoz-Carpintero Luis G. Marin Doris Saez |
| author_facet | Oscar Cartagena Sebastian Parra Diego Munoz-Carpintero Luis G. Marin Doris Saez |
| author_sort | Oscar Cartagena |
| collection | DOAJ |
| description | The existing uncertainties during the operation of processes could strongly affect the performance of forecasting systems, control strategies and fault detection systems when they are not considered in the design. Because of that, the study of uncertainty quantification has gained more attention among the researchers during past decades. From this field of study, the prediction intervals arise as one of the techniques most used in literature to represent the effect of uncertainty over the future process behavior. Thus, researchers have focused on developing prediction intervals based on the use of fuzzy systems and neural networks, thanks to their usefulness for represent a wide range of processes as universal approximators. In this work, a review of the state-of-the-art of methodologies for prediction interval modelling based on fuzzy systems and neural networks is presented. The main characteristics of each method for prediction interval construction are presented and some recommendations are given for selecting the most appropriate method for specific applications. To illustrate the advantages of these methodologies, a comparative analysis of selected methods of prediction intervals is presented, using a benchmark series and real data from solar power generation of a microgrid. |
| first_indexed | 2024-12-13T18:34:43Z |
| format | Article |
| id | doaj.art-3dae7b79d1164623a178ec43f15cc9f0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T18:34:43Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3dae7b79d1164623a178ec43f15cc9f02022-12-21T23:35:24ZengIEEEIEEE Access2169-35362021-01-019233572338410.1109/ACCESS.2021.30560039343299Review on Fuzzy and Neural Prediction Interval Modelling for Nonlinear Dynamical SystemsOscar Cartagena0https://orcid.org/0000-0002-5008-4180Sebastian Parra1Diego Munoz-Carpintero2Luis G. Marin3https://orcid.org/0000-0001-8450-6743Doris Saez4https://orcid.org/0000-0001-8029-9871Department of Electrical Engineering, Faculty of Mathematical and Physical Sciences, University of Chile, Santiago, ChileDepartment of Electrical Engineering, Faculty of Mathematical and Physical Sciences, University of Chile, Santiago, ChileInstitute of Engineering Sciences, Universidad de O’Higgins, Rancagua, ChileDepartment of Electrical and Electronics Engineering, Universidad de Los Andes, Bogotá, ColombiaDepartment of Electrical Engineering, Faculty of Mathematical and Physical Sciences, University of Chile, Santiago, ChileThe existing uncertainties during the operation of processes could strongly affect the performance of forecasting systems, control strategies and fault detection systems when they are not considered in the design. Because of that, the study of uncertainty quantification has gained more attention among the researchers during past decades. From this field of study, the prediction intervals arise as one of the techniques most used in literature to represent the effect of uncertainty over the future process behavior. Thus, researchers have focused on developing prediction intervals based on the use of fuzzy systems and neural networks, thanks to their usefulness for represent a wide range of processes as universal approximators. In this work, a review of the state-of-the-art of methodologies for prediction interval modelling based on fuzzy systems and neural networks is presented. The main characteristics of each method for prediction interval construction are presented and some recommendations are given for selecting the most appropriate method for specific applications. To illustrate the advantages of these methodologies, a comparative analysis of selected methods of prediction intervals is presented, using a benchmark series and real data from solar power generation of a microgrid.https://ieeexplore.ieee.org/document/9343299/Prediction intervalsfuzzy intervalneural network intervalsuncertainty |
| spellingShingle | Oscar Cartagena Sebastian Parra Diego Munoz-Carpintero Luis G. Marin Doris Saez Review on Fuzzy and Neural Prediction Interval Modelling for Nonlinear Dynamical Systems IEEE Access Prediction intervals fuzzy interval neural network intervals uncertainty |
| title | Review on Fuzzy and Neural Prediction Interval Modelling for Nonlinear Dynamical Systems |
| title_full | Review on Fuzzy and Neural Prediction Interval Modelling for Nonlinear Dynamical Systems |
| title_fullStr | Review on Fuzzy and Neural Prediction Interval Modelling for Nonlinear Dynamical Systems |
| title_full_unstemmed | Review on Fuzzy and Neural Prediction Interval Modelling for Nonlinear Dynamical Systems |
| title_short | Review on Fuzzy and Neural Prediction Interval Modelling for Nonlinear Dynamical Systems |
| title_sort | review on fuzzy and neural prediction interval modelling for nonlinear dynamical systems |
| topic | Prediction intervals fuzzy interval neural network intervals uncertainty |
| url | https://ieeexplore.ieee.org/document/9343299/ |
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