TY - JOUR
T1 - Fuzzy Model Identification:A Review and Comparison of Type-1 and Type-2 Fuzzy Systems
AU - Meena Tushir
JO - Journal of Information and Computing Science
VL - 3
SP - 209
EP - 219
PY - 2024
DA - 2024/01
SN - 10
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22546.html
KW - Fuzzy Modeling, Identification, Type-1 Fuzzy Logic, Type-2 Fuzzy Logic. 

AB -  Recently,  a  number  of  extensions  to  classical  fuzzy  logic  systems  (type-1  fuzzy  logic  systems) 
have been attracting interest. One of the most widely used extensions is the interval type-2 fuzzy logic systems. 
An  interval  type-2  TSK  fuzzy  logic  system  can  be  obtained  by  considering  the  membership  functions  of  its 
existed  type-1  counterpart  as  primary  membership  functions  and  assigning  uncertainty  to  cluster  centers, 
standard  deviation  of  Gaussian  membership  functions  and  consequence  parameters.  This  paper  presents  a 
review  and  comparison  of  type-1  fuzzy  logic  system  and  type-2  fuzzy  systems  in  fuzzy  modeling  and 
identification. TSK fuzzy model is considered for both type-1 and type-2 fuzzy systems and model parameters 
are updated using gradient descent method. The experimental study is done on two widely known data, namely 
chemical plant data and the stock market data.