Possibilistic linear regression analysis for fuzzy data

Fuzzy data given by expert knowledge can be regarded as a possibility distribution by which possibilistic linear systems are defined. Recently, it has become important to deal with fuzzy data in connection with expert knowledge. Three formulations of possibilistic linear regression analysis are proposed here to deal with fuzzy data. Since our formulations can be reduced to linear programming problems, the merit of our formulations is to be able to obtain easily fuzzy parameters in possibilistic linear models and to add other constraint conditions which might be obtained from expert knowledge of fuzzy parameters. This approach can be regarded as a fuzzy interval analysis in a fuzzy environment.     

A fuzzy model for linear regression

Doklady Mathematics -     

Intuitionistic fuzzy linear programming and duality: a level sets approach

The paper is concerned with linear programming problems whose input data may be intuitionistic fuzzy (IF) while the values of variables are always real numbers. We propose rather general approach to this type of problems based on level sets, and present recent results for problems in which the notions of feasibility and optimality are based on the IF relations. Special attention is devoted to the weak and strong duality.     

Functional fuzzy clusterwise regression analysis

We propose a functional extension of fuzzy clusterwise regression, which estimates fuzzy memberships of clusters and regression coefficient functions for each cluster simultaneously. The proposed method permits dependent and/or predictor variables to be functional, varying over time, space, and other continua. The fuzzy memberships and clusterwise regression coefficient functions are estimated by minimizing an objective function that adopts a basis function expansion approach to approximating functional data. An alternating least squares algorithm is developed to minimize the objective function. We conduct simulation studies to demonstrate the superior performance of the proposed method compared to its non-functional counterpart and to examine the performance of various cluster validity measures for selecting the optimal number of clusters. We apply the proposed method to real datasets to illustrate the empirical usefulness of the proposed method.     

Intuitionistic fuzzy metric spaces

Using the idea of intuitionistic fuzzy set due to Atanassov [Intuitionistic fuzzy sets. in: V. Sgurev (Ed.), VII ITKR's Session, Sofia June, 1983; Fuzzy Sets Syst. 20 (1986) 87], we define the notion of intuitionistic fuzzy metric spaces as a natural generalization of fuzzy metric spaces due to George and Veeramani [Fuzzy Sets Syst. 64 (1994) 395] and prove some known results of metric spaces including Baire's theorem and the Uniform limit theorem for intuitionistic fuzzy metric spaces.     

A simple method for computation of fuzzy linear regression

We propose a new method for computation of fuzzy regression that is simple and gives good solutions. We consider two cases: First, when only the dependent variable is fuzzy, our approach is given and is compared with those suggested in the literature. Secondly, when both dependent and independent variables are fuzzy, our approach is extended and compared with those given in the literature. In each case, a simple example is used to compare the competing approaches.     

Least-squares estimates in fuzzy regression analysis

Regression is a very powerful methodology for forecasting, which is considered as an essential component of successful OR applications. In this paper an idea stemmed from the classical least squares is proposed to handle fuzzy observations in regression analysis. Based on the extension principle, the membership function of the sum of squared errors is constructed. The fuzzy sum of squared errors is a function of the regression coefficients to be determined, which can be minimized via a nonlinear program formulated under the structure of the Chen–Klein method for ranking fuzzy numbers. To illustrate how the proposed method is applied, three cases, one crisp input-fuzzy output, one fuzzy input-fuzzy output, and one non-triangular fuzzy observations, are exemplified. The results show that the least-squares method of this paper is able to determine the regression coefficients with better explanatory power. Most important, it works for all types of fuzzy observations, not restricted to the triangular one.     

Intuitionistic fuzzy finite state machines

Using the notion of intuitionistic fuzzy sets, the concepts of intuitionistic fuzzy finite state machines (iffsm), intuitionistic successor s, intuitionistic subsystems, intuitionistic submachines, intuitionisticq-twins, and intuitionistic retrievable iffsm are introduce d, and related properties are studied. Relations between intuitionisticq-twins and intuitionisticq-related iffsm are given. A characterization of an intuitionistic retrievable iffsm is provided.     

Intuitionistic fuzzy commutative hyper K-ideals

In this paper first we introduce the notion of intuitionistic fuzzy (weak)hyperK-ideals and also commutative hyperK-ideals of types 1–4 and by some examples we show that there are different notions. Then we obtain some relationships between these notions. Finally by given the notion of the product of two intuitionistic fuzzy commutative hyperK-ideals we give a Decomposition Theorem.     

Intuitionistic supra fuzzy topological spaces

In this paper, We introduce an intuitionistic supra fuzzy closure space and investigate the relationship between intuitionistic supra fuzzy topological spaces and intuitionistic supra fuzzy closure spaces. Moreover, we can obtain intuitionistic supra fuzzy topological space induced by an intuitionistic fuzzy bitopological space. We study the relationship between intuitionistic supra fuzzy closure space and the intuitionistic supra fuzzy topological space induced by an intuitionistic fuzzy bitopological space.     

Intuitionistic fuzzy sublattices and ideals

We study the concept of intuitionistic fuzzy sublattices and intuitionistic fuzzy ideals of a lattice. Some characterization and properties of these intuitionistic fuzzy sublattices and ideals are established. Also we introduce the sum and product of two intuitionistic fuzzy ideals and prove that the sum and product of two Intuitionistic fuzzy ideals of a distributive lattice is again an intuitionistic fuzzy ideal. Moreover, we study the properties of intuitionistic fuzzy ideals under lattice homomorphism.     

Evaluation of fuzzy linear regression models by comparing membership functions

Fuzzy linear regression models can provide an estimated fuzzy number that has a fuzzy membership function. If a point that has the highest membership value from the estimated fuzzy number is not within the support of the observed fuzzy membership function, a decision-maker can have high risk from the estimate. In this study a modification of fuzzy linear regression analysis based on a criterion of minimizing the difference of the fuzzy membership values between the observed and estimated fuzzy numbers is proposed. Two numerical examples are used to evaluate the fuzzy regression models.     

Switching regression analysis by fuzzy adaptive network

A nonparametric switching regression approach is proposed based on the Sugeno fuzzy inference system and a fuzzy adaptive network. The present approach possesses both the ability of handling linguistic terms and the easiness in developing learning algorithms. Two numerical examples are used to illustrate the approach and the convergence behavior is compared with the results in the literature.     

Sensitivity analysis in fuzzy linear programming

In this paper we propose a certain interpretation of a partially fuzzy LP-Problem. This LP-is dualized and the corresponding variables of the Dual are analyzed and an economic interpretation is given.     

Intuitionistic fuzzy quasi-ideals of ordered semigroups

In this paper we define intuitionistic fuzzy quasi-ideals of ordered semigroups. The main result of the paper is a characterization of quasi-ideals in terms of intuitionistic fuzzy quasi-ideals. We also characterize left simple, right simple, and completely regular ordered semigroups in terms of intuitionistic fuzzy quasi-ideals. We study the decomposition of left and right simple ordered semigroups using intuitionistic fuzzy quasi-ideals.     

Intuitionistic fuzzy soft graphs with applications

We present different types of new concepts, including intuitionistic fuzzy soft graphs, complete intuitionistic fuzzy soft graph, strong intuitionistic fuzzy soft graph and self complement of intuitionistic fuzzy soft graph. We describe various methods of their construction, and investigate some of their related properties. We also discuss applications of intuitionistic fuzzy soft graphs in communication network and decision making.