In some statistical process control applications in the real world, quality of a process or a product is characterized by a relationship between two or more variables, which is referred to as profile. Understanding and checking the stability of process over time can be facilitated by monitoring the quality profile in phase I. Sometimes the quality characteristics may be linguistic, imprecise, v...

in this paper, a new method based on parametric form for approximate solu-tion of fuzzy linear matrix equations (flmes) of the form ax = b; where ais a crisp matrix, b is a fuzzy number matrix and the unknown matrix x one,is presented. then a numerical example is presented to illustrate the proposedmodel.

a‎s we know, developing mathematical models and numerical procedures that would appropriately treat and solve systems of linear equations where some of the system's parameters are proposed as fuzzy numbers is very important in fuzzy set theory. for this reason, many researchers have used various numerical methods to solve fuzzy linear systems. in this paper, we define the concepts of midpoint a...

this paper proposes a neuro fuzzy model for analyzing the relationship between contractor’s qualifications and project quality in research projects. the proposed model has been implemented in a research-based organization, iies. cross validation method has been used in order to generate some set of data which have been used for different evaluations. the proposed neuro fuzzy model has dominated...

Applying a repetitive control mechanism to eliminate disturbances in a mechanical control system has been an important subject of investigation. This paper presents a fuzzy theorem approach for the repetitive control law using basis functions feedback. The law adjusted the command given a feedback control system in order to eliminate tracking errors because of a periodic disturbance. This perio...

Abstract: Fuzzy regression models have been applied to several Operations Research applications viz., forecasting and prediction. Earlier works on fuzzy regression analysis obtain crisp regression coefficients for eliminating the problem of increasing spreads for the estimated fuzzy responses as the magnitude of the independent variable increases. But they cannot deal with the problem of non-un...

This paper focuses on modelling fuzzy numbers with meaningful membership functions. More precisely, it proposes a method to construct trapezoidal fuzzy number approximations from raw discrete data. In many applications, input information is numerical, and therefore, particular fuzzy sets, such as fuzzy numbers, hold great interest and relevance in managing data imprecision and vagueness. The pr...

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...