A Novel Approach to T-S Fuzzy Modeling of Nonlinear Dynamic Systems with Uncertainties using Symbolic Interval-Valued Outputs

Zaidi, Salman; Kroll, Andreas

doi:10.1016/j.ifacol.2015.12.294

Cited by 2 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This interval method presented in ( 35) is applied in [40] for the identification of a nonlinear system. A similar strategy was followed in the works of [41], [42], where a fuzzy model is identified for an interval-valued data characterized by confidence intervals obtained from an electro-mechanical throttle valve using the Chebyshev's inequality.…”

Section: ) Min-max Methodsmentioning

confidence: 99%

Review on Fuzzy and Neural Prediction Interval Modelling for Nonlinear Dynamical Systems

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: ) Min-max Methodsmentioning

confidence: 99%