In this paper, we deal with fuzzy random variables for inputs andoutputs in Data Envelopment Analysis (DEA). These variables are considered as fuzzyrandom flat LR numbers with known distribution. The problem is to find a method forconverting the imprecise chance-constrained DEA model into a crisp one. This can bedone by first, defuzzification of imprecise probability by constructing a suitablem...

in this paper, we deal with fuzzy random variables for inputs andoutputs in data envelopment analysis (dea). these variables are considered as fuzzyrandom flat lr numbers with known distribution. the problem is to find a method forconverting the imprecise chance-constrained dea model into a crisp one. this can bedone by first, defuzzification of imprecise probability by constructing a suitablem...

The purpose of this paper is to develop a new two-stage method for fuzzy multi-objective linear program and apply to engineering project portfolio selection. In the fuzzy multi-objective linear program, all the objective coefficients, technological coefficients and resources are trapezoidal fuzzy numbers (TrFNs). An order relationship for TrFNs is introduced by using the interval expectation of...

Suppose we are given a mathematical programming problem in which the functional relationship between the decision variables and the objective function is not completely known. Our knowledge-base consists of a block of fuzzy if-then rules, where the antecedent part of the rules contains some linguistic values of the decision variables, and the consequence part is a linear combination of the cris...

In our fuzzy neural networks fuzzy weights and fuzzy operations are used for training crisp and fuzzy data. Theoretical studies of fuzzy networks where triangular fuzzy numbers are used, show that the output behaviour of these networks can be estimated for arbitrary input data. To make use of these properties we present two learning algorithms for our networks. We implemented and tested them an...

A novel scheme for developing, at low computational cost, neural-fuzzy classifiers based on large-scale, model-based exemplars is outlined. The new method extends the approach that Bezdek applied to train a neural net (NN) Sobel edge classifier by training the NN on the complete population of 3x3 binary image prototypes scored to fuzzy values by a classical operator. We first show that, replaci...

This paper presents a group multi-criteria DEMATEL and VIKOR decision analysis method with interval type-2 fuzzy sets. In order to compare normal fuzzy trapezoidal numbers, we convert them into crisp values using graded mean integration representation. By a case study for selection of business intelligence platform, we prove that the proposed combination is a feasible solution that can work wit...

According to fuzzy arithmetic, general dual fuzzy linear system (GDFLS) cannot be replaced by a fuzzy linear system (FLS). In this paper, we use new notation of fuzzy numbers and convert a GDFLS to two linear systems in crisp case, then we discuss complexity of the proposed method. Conditions for the existence of a unique fuzzy solution to n× n GDFLS are derived. AMS Subject Classification: 34K...

Copyright © 2013 X. Guo and D. Shang.This is an open access article distributed under theCreativeCommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The fuzzy matrix equations ?̃?⊗?̃?⊗?̃? = ?̃? in which ?̃?, ?̃?, and ?̃? arem×m, n×n, andm×n nonnegative LR fuzzy numbers matrices, respectively, are invest...

this study is an investigation of fuzzy linear regression model for crisp/fuzzy input and fuzzy output data. a least absolutes deviations approach to construct such a model is developed by introducing and applying a new metric on the space of fuzzy numbers. the proposed approach, which can deal with both symmetric and non-symmetric fuzzy observations, is compared with several existing models by...