@Article{JICS-10-209,
author = {Meena Tushir},
title = {Fuzzy Model Identification:A Review and Comparison of Type-1 and Type-2 Fuzzy Systems},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {10},
number = {3},
pages = {209--219},
abstract = { 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. 
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22546.html}
}