Measurement results contain different kinds of uncertainty. Besides systematic errors andrandom errors individual measurement results are also subject to another type of uncertainty,so-called emph{fuzziness}. It turns out that special fuzzy subsets of the set of real numbers $RR$are useful to model fuzziness of measurement results. These fuzzy subsets $x^*$ are called emph{fuzzy numbers}. The m...

to investigate pedological criterion on land degradation in quaternary rock units, at first, a part of the shoor watershed area was selected. after distinguishing target area, slope classes, land use and geology maps were created, then map of units were founded by overlaying and crossing these maps. in this research three indices of erodibility, salinity and permeability of soils were considere...

this article, by leaving pre-determined membership function of a fuzzy set which is a basic assumption for such subject, will try to propose a hybrid technique to select the most economic project among alternative projects in fuzziness interest rates condition. in this way, net present worth (npw) would be the economic indicator. this article tries to challenge the assumption of large sample si...

In this paper, for nonlinear, time-varying and delayed system, PID parameters self-tuning fuzzy control was applied to a level process. The method of using VC++ programming to realize fuzzy control algorithm and obtain the discrete fuzzy control table is proposed, which simplified the computing process greatly. Furthermore, combing the inquiry of fuzzy control table and PID function module in P...

A fuzzy number intuitionistic fuzzy set (FNIFS) is a generalization of intuitionistic fuzzy set. The fundamental characteristic of FNIFS is that the values of its membership function and non-membership function are trigonometric fuzzy numbers rather than exact numbers. In this paper, we define some operational laws of fuzzy number intuitionistic fuzzy numbers, and, based on these operational la...

We present a fuzzy optimization problem in business and economics. In this problem, a fuzzy price is determined by using a linear two degree demand functions. The objective is to find the optimal fuzzy revenue, which is derived from the fuzzy price. We use level (λ, 1) interval-valued fuzzy numbers to consider the fuzzy price and the fuzzy revenue. Using signed distance to defuzzify, we can obt...

In this paper we present a general framework to face the problem of evaluate fuzzy quantities. A fuzzy quantity is a fuzzy set that may be non normal and/or non convex. This new formulation contains as particular cases the ones proposed by Fortemps and Roubens [7], Yager and Filev [12, 13] and follows a completely different approach. It starts with idea of “interval approximation of a fuzzy num...

We establish the functional equivalence of a generalized class of Gaussian radial basis function (RBFs) networks and the full Takagi-Sugeno model (1983) of fuzzy inference. This generalizes an existing result which applies to the standard Gaussian RBF network and a restricted form of the Takagi-Sugeno fuzzy system. The more general framework allows the removal of some of the restrictive conditi...

This paper examines the fuzzy system for function approximation in a macroscopic level. The rules of a fuzzy function approximation system are expressed in the form of polynomials such that each rule forms a local approximator. We show that this kind of fuzzy function approximation is equivalent to piecewise polynomial interpolation between turning points when normalized fuzzy function membersh...