Regresar

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

Abstract:

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 pbkp_rediction 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 pbkp_rediction 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 pbkp_rediction interval modelling based on fuzzy systems and neural networks is presented. The main characteristics of each method for pbkp_rediction 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 pbkp_rediction intervals is presented, using a benchmark series and real data from solar power generation of a microgrid.