Providing efficient access to a large user population with variant service requirements in wireless communications networks poses a very challenging problem. Resource allocation in the wireless domain should take into account bandwidth limitations and fading effects inherent to wireless channels, while accommodating for resource constraints encountered in wireline networks. In this paper, a fuz...

We consider the problem of nonlinear filtering under the circumstance of unknown covariance statistic of the measurement noise. A novel adaptive unscented Kalman filter (UKF) integrating variational Bayesian methods and fuzzy logic techniques is proposed in this paper. It is called fuzzy adaptive variational Bayesian UKF (FAVBUKF). Firstly, the sufficient statistics of the measurement noise var...

In many engineering applications, we have to combine probabilistic, interval, and fuzzy uncertainty. For example, in environmental analysis, we observe a pollution level x(t) in a lake at different moments of time t, and we would like to estimate standard statistical characteristics such as mean, variance, autocorrelation, correlation with other measurements. In environmental measurements, we o...

The objective of this paper is to compare probabilistic models and fuzzy set models for design against uncertainty when there is limited information about the statistics of the uncertainty or modeling error. First, we compare the axioms of probabilistic and fuzzy set methods and the rules governing the arithmetic operations that these methods use. Then, we compare the two methods in designing f...

Chemical Process Plant as a complex system requires good scientific knowledge on different issues including Process Safety Analysis (PSA). Fuzzy logic is the collective name for “Fuzzy set analysis” and “Possibility theory”, able to use random or approximate data in the Process Safety Analysis. This paper explains the impact of fuzzy set theory for basic tools used in Process Safety Analysis. I...

Data mining is the analysis step of the "Knowledge Discovery in Databases" process, or KDD. It is the process that results in the discovery of new patterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract knowledge from an existing data set and tra...

Words mean different things to different people, and so are uncertain. We, therefore, need a fuzzy set model for a word that has the potential to capture their uncertainties. In this paper I propose that an interval type-2 fuzzy set (IT2 FS) be used as a FS model of a word, because it is characterized by its footprint of uncertainty (FOU), and therefore has the potential to capture word uncerta...

For Many Years, achieving unambiguous knowledge has been turned to a serious challenge for human being. The aim of this paper is to emphasize situation when classical {true, false} logic is not adequate for data selection and data classification. Linguistic expression like: high salary, young etc are very often used in life and in statistics. The goal of this paper is brief study of fuzzy logic...

Background: Science relies on experimentation to find truth. It demands that conditions remain unchanged for each repetition of an experiment. Thus, medicine relies on ‘probability theory’ based statistics and large double blind controlled randomized clinical trials. The purpose of this study is to discover measures that account for different and changing conditions of individual patients. Thes...

Data mining is defined as the process of extracting or mining knowledge from vast and large database. Data mining is an interdisciplinary field that brings together techniques from machine learning, pattern recognition, statistics, databases, and visualization to address the issue of information extraction from large databases. Bioinformatics is defined as the science of organizing and analyzin...