نتایج جستجو برای: fuzzy linear regression

تعداد نتایج: 816185  

2006
A. K. AL-Othman N. H. Abbasy

`Abstract: -A fuzzy linear state estimation model is employed, which is based on Tanaka's fuzzy linear regression model, for modeling uncertainty in power system state estimation. Both measurements uncertainty as well as parametric uncertainty is considered by fuzzy estimator. The uncertain measurements and the parameters are expressed as fuzzy numbers with a triangular membership function that...

آرادمهر, مریم, خیابانی تنها, بهروز, شفاعی بجستانی, نرگس, نسلی اصفهانی, انسیه,

Background: Diabetes is one of the most dangerous and common diseases of the modern world. Since medical research usually has limited data available and medical data is very ambiguous, it seems appropriate to use the fuzzy model to find out the relationship between input and output in medical data. None of the previous articles of fuzzy regression have been used to predict complications of diab...

2006
Sergio Donoso Amparo Vila

Fuzzy regression models has been traditionally considered as a problem of linear programming. We introduce new models founded on quadratic programming with the aim of overcoming the limitations of linear programming, and that allow to define a great amplitude of wide variety. We verify the existence of multicollinearity in fuzzy regression and we propose a model based on Ridge regression in ord...

This paper presents a new method for regression model prediction in an uncertain environment. In practical engineering problems, in order to develop regression or ANN model for making predictions, the average of set of repeated observed values are introduced to the model as an input variable. Therefore, the estimated response of the process is also the average of a set of output values where th...

2015
Muhammad Ammar Shafi Mohd Saifullah Rusiman

Regression analysis has become popular among several fields of research and standard tools in analysing data. This structure was represented by four commonly statistical models such as multiple linear regression, fuzzy linear regression (Tanaka, 1982), fuzzy linear regression (Ni, 2005) and extended fuzzy linear regression by benchmarking models under fuzziness (Chung, 2012). Colorectal cancer ...

Journal: :iranian journal of fuzzy systems 2014
m. saheli a. hasankhani a. nazari

in the present paper, we study some properties of fuzzy norm of linear operators. at first the bounded inverse theorem on fuzzy normed linear spaces is investigated. then, we prove hahn banach theorem, uniform boundedness theorem and closed graph theorem on fuzzy normed linear spaces. finally the set of all compact operators on these spaces is studied.

2009
A. M. Pashayev

In this paper is shown that the probability-statistic methods application, especially at the early stage of the aviation gas turbine engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence is considered the efficiency of application of new technology Soft Computing at these diagnosing stages with the usin...

Journal: :iranian journal of fuzzy systems 2005
saeid abbasbandy magid alavi

in this paper we present a method for solving fuzzy linear systemsby two crisp linear systems. also necessary and sufficient conditions for existenceof solution are given. some numerical examples illustrate the efficiencyof the method.

Journal: :iranian journal of fuzzy systems 2009
nikbakhsh javadian yashar maali nezam mahdavi-amiri

we present a new model and a new approach for solving fuzzylinear programming (flp) problems with various utilities for the satisfactionof the fuzzy constraints. the model, constructed as a multi-objective linearprogramming problem, provides flexibility for the decision maker (dm), andallows for the assignment of distinct weights to the constraints and the objectivefunction. the desired solutio...

This paper deals with ridge estimation of fuzzy nonparametric regression models using triangular fuzzy numbers. This estimation method is obtained by implementing ridge regression learning algorithm in the La- grangian dual space. The distance measure for fuzzy numbers that suggested by Diamond is used and the local linear smoothing technique with the cross- validation procedure for selecting t...

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