Investigates Sugeno's and Yasukawa's (1993) qualitative fuzzy modeling approach. We propose some easily implementable solutions for the unclear details of the original paper, such as trapezoid approximation of membership functions, rule creation from sample data points, and selection of important variables. We further suggest an improved parameter identification algorithm to be applied instead of the original one. These details are crucial concerning the method's performance as it is shown in a comparative analysis and helps to improve the accuracy of the built-up model. Finally, we propose a possible further rule base reduction which can be applied successfully in certain cases. This improvement reduces the time requirement of the method b...
Abstract—Fuzzy modeling of high-dimensional systems is a challenging topic. This paper proposes an e...
In fuzzy rule-based models acquired from numerical data, redundancy may be present in the form of si...
AbstractA practical problem in the identification of fuzzy systems from data, is the design and the ...
One of the most significant steps in fuzzy modeling of a complex system is Structure Identification....
This paper deals with a simplified version of the evolving Takagi-Sugeno (eTS) learning algorithm - ...
In this paper, a method for constructing Takagi-Sugeno (TS) fuzzy system from data is proposed with ...
Due to its high performance and comprehensibility, fuzzy modelling is becoming more and more popular...
Fuzzy models, especially Takagi-Sugeno (T-S) fuzzy models, have received particular attention in the...
An efficient approach is presented to improve the local and global approximation and modelling capab...
A novel optimal method is developed to improve the identification and estimation of Takagi-Sugeno (T...
Nowadays, Linguistic Modeling is considered to be one of the most important areas of application for...
In this paper, a method for constructing Takagi-Sugeno (TS) fuzzy system from data is proposed with ...
Fuzzy rule base simplification is used to reduce the complexity of fuzzy models identified from the ...
Fuzzy rule base simplification is used to reduce the complexity of fuzzy models identified from the ...
This paper presents a step-by-step approach to reducing the number of fuzzy rules. A weighting index...
Abstract—Fuzzy modeling of high-dimensional systems is a challenging topic. This paper proposes an e...
In fuzzy rule-based models acquired from numerical data, redundancy may be present in the form of si...
AbstractA practical problem in the identification of fuzzy systems from data, is the design and the ...
One of the most significant steps in fuzzy modeling of a complex system is Structure Identification....
This paper deals with a simplified version of the evolving Takagi-Sugeno (eTS) learning algorithm - ...
In this paper, a method for constructing Takagi-Sugeno (TS) fuzzy system from data is proposed with ...
Due to its high performance and comprehensibility, fuzzy modelling is becoming more and more popular...
Fuzzy models, especially Takagi-Sugeno (T-S) fuzzy models, have received particular attention in the...
An efficient approach is presented to improve the local and global approximation and modelling capab...
A novel optimal method is developed to improve the identification and estimation of Takagi-Sugeno (T...
Nowadays, Linguistic Modeling is considered to be one of the most important areas of application for...
In this paper, a method for constructing Takagi-Sugeno (TS) fuzzy system from data is proposed with ...
Fuzzy rule base simplification is used to reduce the complexity of fuzzy models identified from the ...
Fuzzy rule base simplification is used to reduce the complexity of fuzzy models identified from the ...
This paper presents a step-by-step approach to reducing the number of fuzzy rules. A weighting index...
Abstract—Fuzzy modeling of high-dimensional systems is a challenging topic. This paper proposes an e...
In fuzzy rule-based models acquired from numerical data, redundancy may be present in the form of si...
AbstractA practical problem in the identification of fuzzy systems from data, is the design and the ...