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...

this article proposes composition of fuzzy sets theory with discrete-event simulation and puts it into application in order to model uncertain activity duration in simulating a real-world system. the purpose of this paper is to assess the usefulness of fuzzy simulation for modeling uncertain activity duration, especially when insufficient or no sample data is available. in this research, fuzzy ...

this study considers an eoq inventory model with advance payment policy in a fuzzy situation by employing two types of fuzzy numbers that are trapezoidal and triangular. two fuzzy models are developed here. in the first model the cost parameters are fuzzified, but the demand rate is treated as crisp constant. in the second model, the demand rate is fuzzified but the cost parameters are treated ...

comparing the performance of a set of activities or organizations under uncertainty environment has been performed by means of fuzzy data envelopment analysis (fdea) since the traditional dea models require accurate and precise performance data. as regards a method for dealing with uncertainty environment, many researchers have introduced dea models in fuzzy environment. some of these models ar...

We investigate the use of the standard morphological texture characterisation methods, the granulometry and the variogram, in the task of texture classification. These methods are applied to both colour and greyscale texture images. We also introduce a method for minimising the effect of different illumination conditions and show that its use leads to improved classification. The classification...

we use the basic binomial option pricing method but allow someor all the parameters in the model to be uncertain and model this uncertaintyusing fuzzy numbers. we show that with the fuzzy model we can, with areasonably small number of steps, consider almost all possible future stockprices; whereas the crisp model can consider only n + 1 prices after n steps.

In this work, we consider some computational issues related to the minimum mean-squared error (MMSE) prediction of non-Gaussian variables under a spatial generalized linear mixed model (GLMM). This model has been used to model spatial non-Gaussian variables by Diggle et al. (1998) and Zhang (2002), under which MMSE prediction of non-Gaussian variables can be computed. Since the MMSE prediction ...