Assessing accuracy of k ‐step‐ahead prediction of non‐linear dynamics with uncertainties
Yousef Alipouri, Javad Poshtan · IET Signal Processing · 2015
Prediction of k ‐step ahead requires an accurate model for a non‐linear system. If the non‐linearities are simply ignored, there will be the danger of overestimating the output value due to uncertainties. Considering that almost all models have uncertainties, in this study, the accuracy of k ‐step‐ahead prediction is assessed considering the structural, parametric, and algorithmic uncertainties. Then, in order to remove uncertainties from data and achieve an accurate k ‐step‐ahead prediction, interval type‐2 fuzzy set is utilised. This study proposes a strategy for modelling symmetric interval type‐2 fuzzy sets using their uncertainty degrees and centre of gravities. On the basis of these uncertainty measures, a method is introduced for constructing interval type‐2 fuzzy set models using the uncertain interval data. The aim is to provide tools for evaluating a model or a set of models on the basis of predictive accuracy or efficiency for non‐linear dynamic applications with uncertainties. Simulation studies demonstrate the effectiveness of the proposed control scheme.