Automatically constructing type-2 TSK neural fuzzy system based on type-1 fuzzy rules
GAO Jun-long
YUAN Ru-yi
YI Jian-qiang
YING Hao
LI Cheng-dong
Abstract：This paper presents a novel approach to generating an interval type-2 TSK (Takagi-Sugeno-Kang) neural fuzzy system (IT2-TSK-NFS) by using type-1 TSK fuzzy (T1-TSK) rules.This method makes full use of training data sets and those Tl fuzzy rules generated from existing well-behaved self-organizing T1 methods to automatically generate a better performing IT2-TSK-NFS through novel antecedent type transformation and adaptive parameter training algorithms.Meanwhile,the rule number of the IT2-TSK-NFS stays the same as the original Tl's whereas the total number of IT2-FSs in the antecedent is no more than that of the original ones.Two benchmark examples with three different disturbance scenarios are given in experiments.The comparison results show and validate the proposed IT2-TSK-NFS can perform better than original T1-TSK system,and in some cases better than other IT2 self-organizing methods in literature in dealing with system modelling and identification issues under different disturbances.
Keywords：type-2 fuzzy logic systemneural fuzzy systemtype transformationdata drivenmergence
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：16( 1614-1629 )