Fuzzy Model Identification:A Review and Comparison of Type-1 and Type-2 Fuzzy Systems
Authors
Meena Tushir
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.