In this paper the definition of fuzzy anti-normed linear spaces and its basic properties are used to prove some properties of a finite dimensional fuzzy anti-normed linear space.    

The article emphasizes that 3D stochastic positive linear system with delays is asymptotically stable and depends on the sum of the system matrices and at the same time independent on the values and numbers of the delays. Moreover, the asymptotic stability test of this system with delays can be abridged to the check of its corresponding 2D stochastic positive linear systems without delays. Many theorems were applied to prove that asymptotic stability for 3D stochastic positive linear systems with delays are equivalent to 2D stochastic positive linear systems without delays. The efficiency of the given methods is illustrated on some numerical examples. HIGHLIGHTS Various theorems were applied to prove the asymptoti

In this paper the definition of fuzzy normed space is recalled and its basic properties. Then the definition of fuzzy compact operator from fuzzy normed space into another fuzzy normed space is introduced after that the proof of an operator is fuzzy compact if and only if the image of any fuzzy bounded sequence contains a convergent subsequence is given. At this point the basic properties of the vector space FC(V,U)of all fuzzy compact linear operators are investigated such as when U is complete and the sequence ( ) of fuzzy compact operators converges to an operator T then T must be fuzzy compact. Furthermore we see that when T is a fuzzy compact operator and S is a fuzzy bounded operator then the composition TS and ST are fuzzy compact

Necessary and sufficient conditions for the operator equation I AXAX n*, to have a real positive definite solution X are given. Based on these conditions, some properties of the operator A as well as relation between the solutions X andAare given.

In this paper, a self-tuning adaptive neural controller strategy for unknown nonlinear system is presented. The system considered is described by an unknown NARMA-L2 model and a feedforward neural network is used to learn the model with two stages. The first stage is learned off-line with two configuration serial-parallel model & parallel model to ensure that model output is equal to actual output of the system & to find the jacobain of the system. Which appears to be of critical importance parameter as it is used for the feedback controller and the second stage is learned on-line to modify the weights of the model in order to control the variable parameters that will occur to the system. A back propagation neural network is appl

Linear discriminant analysis and logistic regression are the most widely used in multivariate statistical methods for analysis of data with categorical outcome variables .Both of them are appropriate for the development of linear  classification models .linear discriminant analysis has been that the data of explanatory variables must be distributed multivariate normal distribution. While logistic regression no assumptions on the distribution of the explanatory data. Hence ,It is assumed that logistic regression is the more flexible and more robust method in case of violations of these assumptions.

In this paper we have been focus for the comparison between three forms for classification data belongs

This study offers a new Mixed Meta Heuristics algorithm (HGSABAT) for estimating the parameter values of each of the six categories of Non-Linear regression models examined (Misrald, Meyer4, Meyer7, Militky4, Militky2, and MGH09) by combining the Gravitational Search Algorithm and Bat Algorithm. Some models have different numbers of parameters. For example, the Misrald and Militky2 models of the Non-Linear Regression model have two parameters (Bl, B2). In contrast, the MGH09 and Militky4 models have four parameters (MGHl, MGH2, MGH3, and MGH4), in which location as the Meyer4 and Meyer7 models have three attributes (Meyerl, MGH2, and MGH3). To examine the effectiveness of the suggested Hybrid Meta Heuristics algorithm (HGSABAT), a simulatio

This study relates to  the estimation of  a simultaneous equations system for the Tobit model where the dependent variables  ( )  are limited, and this will affect the method to choose the good estimator. So, we will use new estimations methods  different from the classical methods, which if used in such a case, will produce biased and inconsistent estimators which is (Nelson-Olson) method  and  Two- Stage limited dependent variables(2SLDV) method  to get of estimators that hold characteristics the good estimator .

That is , parameters will be estim

Background: Antibacterial action of root canal filling is an important factor for successful root canal treatment, so the aim of the study was to identify and to compare the antimicrobial effect of new sealer (GuttaFlow) to commonly used endodontic sealers (AH Plus, Apexit and EndoFill) against four endodontic microbes. Materials and methods: Twenty patients aged (30-40) years with infected root canals were selected. Four types of microorganisms were isolated from root canals (E faecalis, Staphylococcus aureus, E coli and Candida albicans) and cultured on Mueller Hinton agar Petri-dishes. After identification and isolation of bacterial species, agar diffusion method was used to assess the antibacterial action of four contemporary endodontic

Linear regression is one of the most important statistical tools through which it is possible to know the relationship between the response variable and one variable (or more) of the independent variable(s), which is often used in various fields of science. Heteroscedastic is one of the linear regression problems, the effect of which leads to inaccurate conclusions. The problem of heteroscedastic may be accompanied by the presence of extreme outliers in the independent variables (High leverage points) (HLPs), the presence of (HLPs) in the data set result unrealistic estimates and misleading inferences. In this paper, we review some of the robust