@Article{id:Siminski2017Interval,
  Author                   = {Krzysztof Siminski},
  Title                    = {Interval Type-2 Neuro-Fuzzy System with Implication-based Inference Mechanism},
  Journal                  = {Expert Systems With Applications},
  doi                      = {10.1016/j.eswa.2017.02.046}, 
  year                     = {2017},
  volume                   = {79C},
  pages                    = {140-152},
  abstract = "Neuro-fuzzy systems have been proved to be an efficient tool 
  for modelling real life systems. They are precise and have ability to 
  generalise knowledge from presented data. Neuro-fuzzy systems use fuzzy sets
  – most commonly type-1 fuzzy sets. Type-2 fuzzy sets model uncertainties 
  better than type-1 fuzzy sets because of their fuzzy membership function. 
  Unfortunately computational complexity of type reduction in general type-2 
  systems is high enough to hinder their practical application. This burden 
  can be alleviated by application of interval type-2 fuzzy sets. The paper 
  presents an interval type-2 neuro-fuzzy system with interval type-2 fuzzy 
  sets both in premises (Gaussian interval type-2 fuzzy sets with uncertain 
  fuzziness) and consequences (trapezoid interval type-2 fuzzy set). The inference
  mechanism is based on the interval type-2 fuzzy Łukasiewicz, Reichenbach, 
  Kleene-Dienes, or Brouwer–Gödel implications. The paper is accompanied by 
  numerical examples. The system can elaborate models with lower error rate 
  than type-1 neuro-fuzzy system with implication-based inference mechanism. 
  The system outperforms some known type-2 neuro-fuzzy systems.",
}