Data Envelopment Analysis (DEA) is a technique used to compare efficiency in various sectors such as hospitals, chain stores, and dealerships. It represents a set of linear programming techniques and uses deter-ministic data (inputs and outputs), in stable conditions. The DEA technique cannot be used when there is data with indeterministic nature, or when there is an environment with dynamic co...

in this paper, we consider portfolio selection problem in which security returns are regarded as fuzzy variables rather than random variables. we first introduce a concept of absolute deviation for fuzzy variables and prove some useful properties, which imply that absolute deviation may be used to measure risk well. then we propose two mean-absolute deviation models by defining risk as abs...

The success and proliferation of the Semantic Web depends heavily on construction of Web ontologies. However, classical ontology construction approaches are not sufficient for handling imprecise and uncertain information that is commonly found in many application domains. Therefore, great efforts on construction of fuzzy ontologies have been made in recent years. In this paper, we propose a for...

Amongst possible choices for identifying complicated processes for prediction, simulation, and approximation applications, high-order Takagi-Sugeno (TS) fuzzy models are fitting tools. Although they can construct models with rather high complexity, they are not as interpretable as first-order TS fuzzy models. In this paper, we first propose to use Deformed Linear Models (DLMs) in consequence pa...

In this paper, ‎the multi-sensor fault diagnosis in the exhaust temperature sensors of a V94.2 heavy duty gas turbine is presented‎. ‎A Laguerre network-based fuzzy modeling approach is presented to predict the output temperature of the gas turbine for sensor fault diagnosis‎. Due to the nonlinear dynamics of the gas turbine, in these models the Laguerre filter parts are related to the linear d...

in recent years, various time series models have been proposed for financial markets forecasting. in each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. many researchers have compared different time series models together in order to determine more efficient ...