In multiple regression, different techniques are available to deal with the situation where predictors large in number, and multicollinearity exists among them. Some of these approaches rely on correlation others depend principal components. To cope influential observations (outliers, leverage, or both) data matrix for regression purposes, two proposed this paper. These Robust Correlation Based...

The present article formulates the scenarios that the organization will be probably facing with, using the uncertain factors in business environment, and it also selects the most robust strategies of organization for dealing with the formulated scenarios using the fuzzy information expressed by the experts in fuzzy inference system. The present article aims to provide a method enabling the scen...

The theoretical background for abstract formalization of the vague phenomenon of complex systems is the fuzzy set theory. In the paper, vague data is defined as specialized fuzzy sets fuzzy numbers and a fuzzy linear regression model is described as a fuzzy function with fuzzy numbers as vague regression parameters. To identify the fuzzy coefficients of the model, the genetic algorithm is used....

-Robust control has long been the purview of quantitative linear control techniques, while qualitative, symbolic control has been deemed more suitable to obtaining complex control objectives that require only low output precision. The intelligent techniques of Fuzzy control have, however, shown promise in obtaining results comparable to those obtained from H∞ and H2 robust control techniques. O...

In this paper, we develop a new control method that brings together the advantages of fuzzy logic and sliding mode control. First, we introduce a non-chattering robust sliding mode control. Then, in order to improve the performance of the controller a fuzzy logic algorithm is integrated with the sliding mode controller. This algorithm decides the slope of the sliding surface of the sliding mode...

We construct a fuzzy varying coefficient bilinear regression model to deal with the interval financial data and then adopt the least-squares method based on symmetric fuzzy number space. Firstly, we propose a varying coefficient model on the basis of the fuzzy bilinear regression model. Secondly, we develop the least-squares method according to the complete distance between fuzzy numbers to est...

Fuzzy clusterwise regression has been a useful method for investigating cluster-level heterogeneity of observations based on linear regression. This method integrates fuzzy clustering and ordinary least-squares regression, thereby enabling to estimate regression coefficients for each cluster and fuzzy cluster memberships of observations simultaneously. In practice, however, fuzzy clusterwise re...