Fuzzy rule interpolation is an important technique for performing inferences with sparse rule bases. Even when given observations have no overlap with the antecedent values of any rule, fuzzy rule interpolation may still derive a conclusion. Nevertheless, fuzzy rule interpolation can only handle fuzziness but not roughness. Rough set theory is a useful tool to deal with incomplete knowledge, wh...

Results from a set o f questionnaires designed to compare methods of obtaining membership functions were analyzed to identify general trends in fuzziness. It was found that fuzziness in comprehension is ahvays reflected in the answer; concepts such as difficulty, imprecision, and shape of membership functions are clearly interrelated; and the transition between crisp extremes through fuzziness ...

The rapid change of the product life cycle is making the parameters of the supply chain models more and more uncertain. Therefore, we consider the coordination mechanisms between one manufacturer and one retailer in a fuzzy decision marking environment, where the parameters of the models can be forecasted and expressed as the triangular fuzzy variables. The centralized decision-making system, t...

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...

In this study, fuzzy logic was implemented to formulate the fuzziness of layer boundaries for a two-layered clay soil. A field of two-layered clay with fuzzy boundaries between layers was generated, and then the bearing capacity of strip footing on this field was calculated by the assumption of plane-strain conditions. The Mohr-Coulomb failure criterion was used and bearing capacity calculation...

Quality function deployment (QFD) is a customer-driven approach, widely used to develop or process new product to maximize customer satisfaction. Last researches used linear physical programming (LPP) procedure to optimize QFD; however, QFD issue involved uncertainties, or fuzziness, which requires taking them into account for more realistic study. In this paper, a set of fuzzy data is used to ...

In applications there occur different forms of uncertainty. The twomost important types are randomness (stochastic variability) and imprecision(fuzziness). In modelling, the dominating concept to describe uncertainty isusing stochastic models which are based on probability. However, fuzzinessis not stochastic in nature and therefore it is not considered in probabilisticmodels.Since many years t...