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...

in this paper, we introduce a takagi-sugeno (ts) fuzzy model which is derived from a typical multi-layer perceptron neural network (mlp nn). at first, it is shown that the considered mlp nn can be interpreted as a variety of ts fuzzy model. it is discussed that the utilized membership function (mf) in such ts fuzzy model, despite its flexible structure, has some major restrictions. after modify...

this ability in fuzzy uml, practically leaves the customers and market’s need without response in this important and vital area. here, the available sequence diagrams in fuzzy uml will map into fuzzy petri net. however, the formal models ability will be added to the semi-formal fuzzy uml. this formalization will add the automatic processing ability to the semi-formal fuzzy uml. further more, ...

In order to model capacitated location-allocation problem with fuzzy demands, three types of fuzzy programming models — fuzzy expected cost minimization model, fuzzy α-cost minimization model, and credibility maximization model — are proposed according to different decision criteria. For solving these models, some hybrid intelligent algorithms are also designed. Finally, several numerical exper...

In a stochastic and fuzzy environment, two kinds of stopping models are discussed and compared. The optimal fuzzy stopping times are given under the assumptions of monotonicity and regularity for stopping rules. Also, we find that fuzzy stopping times are favored in a comparison between fuzzy and classical stopping models. Q 2000 Academic Press

this paper presents the basic concepts of stability in fuzzy linguistic models. theauthors have proposed a criterion for bibo stability analysis of fuzzy linguistic modelsassociated to linear time invariant systems [25]-[28]. this paper presents the basic concepts ofstability in the general nonlinear and linear systems. this stability analysis method is verifiedusing a benchmark system analysis.

ABSTRACT: In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve oxygen concentration at the downstream of Mathura City in India. The data used are dissolved oxygen, pH, biological oxygen demand and water...

in this study, several data-driven techniques including system identification, adaptive neuro-fuzzy inference system (anfis), artificial neural network (ann) and wavelet-artificial neural network (wavelet-ann) models were applied to model rainfall-runoff (rr) relationship. for this purpose, the daily stream flow time series of hydrometric station of hajighoshan on gorgan river and the daily rai...

This article integrates fuzzy set theory in Data Envelopment Analysis (DEA) framework to compute technical efficiency scores when input and output data are imprecise. In conventional DEA inputs and outputs data are precise. However, traffic and transportation take place in an uncertain environment and input and output data might be imprecise. This article proposes a possibility approach for sol...

A hybrid neural network regression models with unsupervised fuzzy clustering is proposed for clustering nonparametric regression models for datasets. In the new formulation, (i) the performance function of the neural network regression models is modified such that the fuzzy clustering weightings can be introduced in these network models; (ii) the errors of these network models are feed-backed i...