In performing a fuzzy multiple linear regression model, important topics are: to measure the fitting quality of the model and to find the “best” set of input variables that explain the variation in the observed system responses. In this paper, by considering an exploratory approach, to express the quality of fit of a fuzzy linear regression model, a coefficient of multiple determination R2 for ...

This paper studies the control synthesis problem of Takagi–Sugeno (T–S) fuzzy systems. By splitting the premise variable spaces and using the properties of fuzzy sets, a new control scheme is proposed based on a new class of fuzzy Lyapunov functions, and a convex condition for designing fuzzy controllers is given, where the new fuzzy Lyapunov functions and fuzzy controllers are constructed base...

A random fuzzy variable is a function from a credibility space to the set of random variables. Chance distribution is a type of mathematical description of random fuzzy variables. This paper presents a sufficient and necessary condition for chance distribution of random fuzzy variables.

The above paper presents a rather complicated mechanism allowing fuzzy evidence to be aggregated when fuzzy inferences are made about the same fuzzy variable by different rules. The purpose of this comment is to present that the FuzzyCLIPS address this problem in simpler way.

In this paper we introduce an algebraic fuzzy equation of degree n with fuzzy coefficients and crisp variable, and we present an iterative method to find the real roots of such equations, numerically. We present an algorithm to generate a sequence that can be converged to the root of an algebraic fuzzy equation. 2007 Elsevier Inc. All rights reserved. MSC: 12E12; 39B22; 65H05

A novel variable-rate vector quantizer (VQ) design algorithm using both fuzzy and competitive learning technique is presented. The algorithm enjoys better rate-distortion performance than that of other existing fuzzy clustering and competitive learning algorithms. In addition, the learning algorithm is less sensitive to the selection of initial reproduction vectors. Therefore, the algorithm can...

In this study, trajectory control of the Variable Loaded Servo (VLS) system is performed by using a Fuzzy Logic based Iterative Learning Control (ILC) method. In the study, a Iterative Learning PID (IL-PID) Controller is used as the iterative learning control structure. Also, a fuzzy adjustment mechanism has been added to the control system for specify the initial parameter of the IL-PID contro...

Variable threshold concept lattice (VTCL) was proposed by Ma et al.(2006), which provide a new parameterized way to obtain formal concepts from data with fuzzy attributes. Axiomatic Fuzzy Set (AFS) algebras were proposed by Liu (1998), which are new semantic methodology relating to the fuzzy theory. In this paper, in order to explore the relationship between the AFS algebras and VTCL, three alg...

There are many applications dealing with incomplete data sets that take different approaches to making imputations for missing values. Most tackle the problem for numerical input variables in the data set. However, when there are two types of input variables, numerical and categorical, the state of the art has provided no clear solutions. This paper presents a proposal for handling incomplete n...

We compare two methods regarding the factorization problem of binary, triadic, real and fuzzy data, namely Hierarchical Classes Analysis and the formal concept analytical approach to Factor Analysis. Both methods search for the smallest set of hidden variables, called factors, to reduce the dimensionality of the attribute space which describes the objects without losing any information. First, ...