Parsimony is very important in system modeling as it is closely related to model interpretability. In this paper, a scheme for constructing accurate and parsimonious fuzzy models by generating distinguishable fuzzy sets is proposed, in which the distinguishability of input space partitioning is measured by a so-called "local" entropy. By maximizing this entropy measure the optimal number of merged fuzzy sets with good distinguishability can be obtained, which leads to a parsimonious input space partitioning while preserving the information of the original fuzzy sets as much as possible. Different from the existing merging algorithms, the proposed scheme takes into account the information provided by input-output samples to optimize input sp...
Nowadays, the growing amounts of collected data enable the training of machine learning models that ...
We introduce in this paper a new formulation of the regularized fuzzy c-means (FCM) algorithm which ...
Fuzzy rule-based systems are effective tools for acquiring knowledge from data and represent it in a...
In this paper, a method for constructing Takagi-Sugeno (TS) fuzzy system from data is proposed with ...
In this paper, a method for constructing Takagi-Sugeno (TS) fuzzy system from data is proposed with ...
In recent years, we have witnessed a strong emphasis on high performance and precision of fuzzy syst...
Distinguishability is a semantic property of fuzzy sets that has a great relevance in the design of ...
Two measures that quantify distinguishability of fuzzy sets are addressed in this paper: similarity,...
This paper aims at providing an in-depth overview of designing interpretable fuzzy inference models ...
Abstract—Fuzzy modeling of high-dimensional systems is a challenging topic. This paper proposes an e...
A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a prior...
In the literature of information theory, there is necessity for comparing the different measures of ...
AbstractThe use of fuzzy set-theoretic measures is explored here in the context of data envelopment ...
Fuzzy logic has been applied successfully to systems modeling for ages. One of its main advantages i...
One of the most important aspects of fuzzy systems is that they are easily understandable and inter...
Nowadays, the growing amounts of collected data enable the training of machine learning models that ...
We introduce in this paper a new formulation of the regularized fuzzy c-means (FCM) algorithm which ...
Fuzzy rule-based systems are effective tools for acquiring knowledge from data and represent it in a...
In this paper, a method for constructing Takagi-Sugeno (TS) fuzzy system from data is proposed with ...
In this paper, a method for constructing Takagi-Sugeno (TS) fuzzy system from data is proposed with ...
In recent years, we have witnessed a strong emphasis on high performance and precision of fuzzy syst...
Distinguishability is a semantic property of fuzzy sets that has a great relevance in the design of ...
Two measures that quantify distinguishability of fuzzy sets are addressed in this paper: similarity,...
This paper aims at providing an in-depth overview of designing interpretable fuzzy inference models ...
Abstract—Fuzzy modeling of high-dimensional systems is a challenging topic. This paper proposes an e...
A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a prior...
In the literature of information theory, there is necessity for comparing the different measures of ...
AbstractThe use of fuzzy set-theoretic measures is explored here in the context of data envelopment ...
Fuzzy logic has been applied successfully to systems modeling for ages. One of its main advantages i...
One of the most important aspects of fuzzy systems is that they are easily understandable and inter...
Nowadays, the growing amounts of collected data enable the training of machine learning models that ...
We introduce in this paper a new formulation of the regularized fuzzy c-means (FCM) algorithm which ...
Fuzzy rule-based systems are effective tools for acquiring knowledge from data and represent it in a...