This paper explores the topic of fuzzy clustering, feature selection, and membership function optimization. Feature selection plays a crucial role for all fuzzy clustering applications, as the selection of appropriate features determines the quality of the resulting clusters. We will show how fuzzy clustering can be applied to data mining problems by introducing some of the most commonly used c...

This paper proposes a new type fuzzy neural systems, denoted IT2RFNS-A interval type-2 recurrent fuzzy neural system with asymmetric membership function , for nonlinear systems identification and control. To enhance the performance and approximation ability, the triangular asymmetric fuzzy membership function AFMF and TSK-type consequent part are adopted for IT2RFNS-A. The gradient information ...

Extracting fuzzy rules from data allows relationships in the data to be modeled by "if-then" rules that are easy to understand, verify, and extend. This paper presents methods for extracting fuzzy rules for both function approximation and pattern classification. The rule extraction methods are based on estimating clusters in the data; each cluster obtained corresponds to a fuzzy rule that relat...

Reinforcement learning is one of the more attractive machine learning technologies, due to its unsupervised learning structure and ability to continually learn even as the environment it is operating in changes. This ability to learn in an unsupervised manner in a changing environment is applicable in complex domains through the use of function approximation of the domain’s policy. The function...

This paper connects two thoroughly investigated universal approximator techniques to each other. Recently, it has been shown that the input-output function of the general fuzzy KH interpolation method [1, 2] as well as its modification [3] are stable in the mathematical sense, or in other words, they can be considered as universal approximators with respect to the Lp (p ∈ [1,∞]) norm in the spa...

Hierarchical structures and uncertainty measures are two main aspects in granular computing, approximate reasoning and cognitive process. Typical hesitant fuzzy sets, as a prime extension of fuzzy sets, are more flexible to reflect the hesitance and ambiguity in knowledge representation and decision making. In this paper, we mainly investigate the hierarchical structures and uncertainty measure...

Abstract We point out that B spline basis functions are naturally de ned membership functions for fuzzy logic systems i e the speci cation of these functions depends only on the partition points of each linguistic variables no more necessarily also on the additional parameters if normal set functions are used Based on B spline basis functions a fuzzy controller can be constructed which works li...

The purpose of the present work is to establish a one-to-one correspondence between the family of interval type-2 fuzzy reflexive/tolerance approximation spaces and the family of interval type-2 fuzzy closure spaces.

In digital-based information boom, the fuzzy covering rough set model is an important mathematical tool for artificial intelligence, and how to build the bridge between the fuzzy covering rough set theory and Pawlak’s model is becoming a hot research topic. In this paper, we first present the γ−fuzzy covering based probabilistic and grade approximation operators and double-quantitative approxim...

Abstract The well-known Karush-Kuhn-Tucker theorem can be used, as in the fuzzy case, to find the trapezoidal approximation of a given intuitionistic fuzzy number. The method is quite technical such that obtaining the trapezoidal approximation of an intuitionistic fuzzy numbers from the trapezoidal approximation of a fuzzy number is proposed in the present paper. Among the advantages of this me...