Interval type-2 neuro-fuzzy system with implication-based inference mechanism

被引：25
|
作者：
Siminski, Krzysztof [1 ]
机构：
[1] Silesian Tech Univ, Inst Informat, Ul Akad 16, PL-44100 Gliwice, Poland
来源：
EXPERT SYSTEMS WITH APPLICATIONS | 2017年 / 79卷
关键词：
Implication-based rules; Fuzzy inference systems; Neuro-fuzzy systems; Interval type-2 fuzzy sets; LOGIC SYSTEMS; ALGORITHM; INTERPOLATION; MODEL; NETWORK; SETS;
D O I：
10.1016/j.eswa.2017.02.046
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 Lukasiewicz, Reichenbach, Kleene-Dienes, or Brouwer-Godel 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. (c) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码：140 / 152
页数：13
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