Statistical regression analysis is a powerful and reliable method to determine the impact of
one or several independent variable (s) on a dependent variable. It is the most widely used …

This study compares building electric energy prediction approaches that use a traditional
statistical method (linear regression) and artificial neural network (ANN) algorithms. We …

The main characteristics of the real-world decision-making problems facing humans today
are multidimensional and have multiple objectives including eco nomic, environmental …

In the previous chapter, we have discussed a variety of computationally efficient approaches
for solving crisp multiple objective decision making problems. However, the input data, such …

Average load forecasting errors for the holidays are much higher than those for weekdays.
So far, many studies on the short-term load forecasting have been made to improve the …

Load forecasting has been a conventional and important process in electric utilities since the
early 20 th century. Due to the deregulation of the electric utility industry, the utilities tend to …

Performance analysis has become a vital part of the management practices in the banking
industry. There are numerous applications using DEA models to estimate efficiency in …

In this paper, we introduce a new class of fuzzy linear regression models based on Tanaka's
approach. Unlike in the Tanaka model, here all training data influence the estimated interval …

Fuzzy linear regression provides means for tackling regression problems lacking a
significant amount of data for determining regression models and with vague relationships …

The concepts of fuzzy set and of its applications has become a battlefield of conflicting
opinions, since its steady development during the sixties. On the one hand, in the camp of …