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 …

The necessity of modelling is well established since the structural identification of a process
is essential in analysis, control and prediction. In the past, limited information on system …

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 …

When we use linear programming in possibilistic regression analysis, some coefficients tend
to become crisp because of the characteristic of linear programming. On the other hand, a …

In this paper, we first propose an architecture of neural networks that have interval weights
and interval biases. A neural network with the proposed architecture maps an input vector of …

There are certain circumstances under which the application of statistical regression is not
appropriate or even feasible because it makes rigid assumptions about the statistical …

This handbook provides information about fundamental aspects of the field and explores the
myriad applications of fuzzy logic techniques and methods. It presents basic conceptual …

Regression analysis is a statistical method employed to establish the relationship between"
independent variables" and" dependent variables." This widely utilized analysis technique is …

Land subsidence is mainly caused by excessive groundwater abstraction from aquifers. This
study introduces Dynamic Subsidence Vulnerability Index (DSVI) by estimating possible …

Let us review conventional regression analysis based on probability models so as to
contrast differences from fuzzy regression analysis to be described later. Generally …