The objective of our research is to build a statistical test that can evaluate different risks of a portfolio selection model with fuzzy data. The central points and radiuses of fuzzy numbers are used to determine the portfolio selection model, and we statistically evaluate the best return by a fuzzy statistical test. Empirical studies are presented to illustrate the risk evaluation of the port...

Molodstov introduced soft sets to deal with uncertainties and not clear objects. Researchers developed the relationship between soft sets and information systems. Fuzzification of the notions of soft sets and soft topology have been discussed in the literature. In this paper, we define and establish the topological structures of fuzzy soft semi-open sets and fuzzy soft semi-closed sets. We intr...

In this paper, we consider a nonparametric Shewhart chart for fuzzy data. We utilize the fuzzy data without transforming them into a real-valued scalar (a representative value). Usually fuzzy data (described by fuzzy random variables) do not have a distributional model available, and also the size of the fuzzy sample data is small. Based on the bootstrap methodology, we design a nonparametric S...

In this research paper we proposed a novel technique for detection of brain tumour by using brightness preserving dynamic fuzzy histogram equalization. The fuzzy histogram equalization uses fuzzy statistics of digital images for their representation. The technique enables to handles the imprecision of gray level value in better way resulting the highlight to hidden as well as blur part of the i...

In this paper, we examine and compare the performance of four fuzzy rule generation methods on Wisconsin breast cancer data [2]. These methods were reported by Ishibuchi [1] et al. For the diagnosis of breast cancer, the determination of the presence of benign/malignant breast tumors represents a very complex problem (even for an experienced cytologist). The goal of this paper is to compare and...

In this paper, we develop fuzzy, possibilistic hypothesis tests for testing crisp hypotheses a distribution parameter from data. these tests, fuzzy statistics are used, which produced by the possibility of estimated parameter, constructed known confidence intervals. The results in much better agreement with than ones respective popular book on statistics, uses critical values. We also present a...

According to the statistical random sampling method, estimation has been taken confidence interval into account in statistics due to the unknown probability for the deviations of point estimate. In terms of fuzziness or vagueness, confidence interval is defined as a range of numerical values, not a single value, so we have to discover an estimate for that interval at first. Based on the above-m...

This paper is a fine review of various aspects related to the statistical handling of “ontic” random fuzzy sets by the means of appropriate distances. It is quite comprehensive and helpful, as it clarifies the status of fuzzy sets in such methods, explains the advantages of using a distance-based approach, specifies the pitfalls in which one should not fall when dealing with “ontic” random fuzz...

In this paper we propose a novel training algorithm for RBF networks that is based on extended kalman filter and fuzzy logic.After the user choose how many prototypes to include in the network, the extended kalman filter simultancously solves for the prototype vectors and the weight matrix.The fuzzy logic is used to cope with the devergence problem caused by the insufficiently known a priori fi...

This study is intended to improve the lack of recruitment processes as well as reduce individual senses of supervisory level by fuzzy logic and Analytic Hierarchy Process methods. This study tries to identify appropriate personality traits and key professional skills through the information statistics and analysis of Analytic Hierarchy Process in order to expect the recruitment process be more ...