fuzzy linear programming problem occur in many elds such as mathematical modeling, control theory and management sciences, etc. in this paper we focus on a kind of linear programming with fuzzy numbers and variables namely fully fuzzy linear programming (fflp) problem, in which the constraints are in inequality forms. then a new method is proposed to ne the fuzzy solution for solving (fflp). ...

This paper presents a modeling approach which couples fuzzy object-oriented database modeling with fuzzy logic. The modeling approach introduced here handles fuzziness at attribute, object/class and class/superclass levels in addition to fuzziness in class/class relationships and various associations among classes. We utilize logical rules to define some of the crisp/fuzzy relationships and ass...

representation and modeling of economic uncertainty is addressed by different modeling methods, namely stochastic variables and probabilities, interval analysis, and fuzzy numbers, in particular triple estimates. fo-cusing on discounted cash flow analysis numerical results are presented, comparisons are made between alter-native modeling methods, and characteristics of the methods are discussed.

Exploring the growing interest in extending the theory of probability and statistics to allow for more flexible modeling of uncertainty, ignorance, and fuzziness, the properties of fuzzy modeling are investigated for statistical signals, which benefit from the properties of fuzzy modeling. There is relatively research in the area, making explicit identification of statistical/stochastic fuzzy m...

Enhanced fuzzy modeling by multivariable fuzzy membership functions is described. From the interpretability issues viewpoint conventionally fuzzy modeling is carried out by means of decomposition of multivariable membership functions via projections on each variable component. However, due to decomposition there involves an error while reconstructing the model output from the contributions of e...

Noise is source of ambiguity for fuzzy systems. Although being an important aspect, the effects of noise in fuzzy modeling have been little investigated. This paper presents a set of tests using three well-known fuzzy modeling algorithms. These evaluate perturbations in the extracted rule-bases caused by noise polluting the learning data, and the corresponding deformations in each learned funct...

Fuzzy modeling has many advantages over the non-fuzzy methods, such as robustness against uncertainties and less sensitivity to the varying dynamics of nonlinear systems. Data-driven fuzzy modeling needs to extract fuzzy rules from the input/output data, and train the fuzzy parameters. This paper takes advantages from deep learning, probability theory, fuzzy modeling, and extreme learning machi...

A special method for fuzzy-logic-based system modeling is presented to develop multi-variable fuzzy models on the basis of the system's measured input and output data. The global modeling problem is proposed to be solved in two steps: by fuzzy model connguration and fuzzy model identiication. The fuzzy model connguration procedure is characterized by preliminary considerations leading to the de...

A technique for the modeling of nonlinear control processes using fuzzy modeling approach based on the Takagi–Sugeno fuzzy model with a combination of genetic algorithm and recursive least square is proposed. This paper discusses the identification of the parameters at the antecedent and consequent parts of the fuzzy model. For the antecedent fuzzy parameters, genetic algorithm is used to tune ...

In this paper, we used the fuzzy set theory for modeling flexible constraints and uncertain data in nurse scheduling problems and proposed a fuzzy linear model for nurse rostering problems. The developed model can produce rosters that satisfy hospital objectives, ward requirements and staff preferences by satisfying their requests as much as possible. Fuzzy sets are used for modeling demands of...