Power Muirhead Mean Operators for Interval-Valued Linear Diophantine Fuzzy Sets and Their Application in Decision-Making Strategies

It is quite beneficial for every company to have a strong decision-making technique at their disposal. Experts and managers involved in decision-making strategies would particularly benefit from such a technique in order to have a crucial impact on the strategy of their company. This paper considers the interval-valued linear Diophantine fuzzy (IV-LDF) sets and uses their algebraic laws. Furthermore, by using the Muirhead mean (MM) operator and IV-LDF data, the IV-LDF power MM (IV-LDFPMM) and the IV-LDF weighted power MM (IV-LDFWPMM) operators are developed, and some special properties and results demonstrated. The decision-making technique relies on objective data that can be observed. Based on the multi-attribute decision-making (MADM) technique, which is the beneficial part of the decision-making strategy, examples are given to illustrate the development. To demonstrate the advantages of the developed tools, a comparative analysis and geometrical interpretations are also provided.

Modelling Geometric Measure of Variation About the Population Mean

Measures of dispersion are important statistical tool used to illustrate the distribution of datasets. These measureshave allowed researchers to define the distribution of various datasets especially the measures of dispersion from the mean.Researchers and mathematicians have been able to develop measures of dispersion from the mean such as mean deviation, variance and standard deviation. However, these measures have been determined not to be perfect, for example, variance give average of squared deviation which differ in unit of measurement as the initial dataset, mean deviation gives bigger average deviation than the actual average deviation because it violates the algebraic laws governing absolute numbers, while standarddeviation is affected by outliers and skewed datasets. As a result, there was a need to develop a more efficient measure of variation from the mean that would overcome these weaknesses. The aim of this paper was to model a geometric measure of variation about the population mean which could overcome the weaknesses of the existing measures of variation about the population mean. The study was able to formulate the geometric measure of variation about the population mean that obeyedthe algebraic laws behind absolute numbers, which was capable of further algebraic manipulations as it could be used further to estimate the average variation about the mean for weighted datasets, probability mass functions and probability density functions. Lastly, the measure was not affected by outliers and skewed datasets. This shows that the formulated measure was capable of solving the weaknesses of the existing measures of variation about the mean

Boolean algebra

Boolean algebra, or the algebra of logic, was devised by the English mathematician George Boole (1815–64) and embodies the first successful application of algebraic methods to logic. Boole seems to have had several interpretations for his system in mind. In his earlier work he thinks of each of the basic symbols of his ‘algebra’ as standing for the mental operation of selecting just the objects possessing some given attribute or included in some given class; later he conceives of these symbols as standing for the attributes or classes themselves. In each of these interpretations the basic symbols are conceived as being capable of combination under certain operations: ‘multiplication’, corresponding to conjunction of attributes or intersection of classes; ‘addition’, corresponding to (exclusive) disjunction or (disjoint) union; and ‘subtraction’, corresponding to ‘excepting’ or difference. He also recognizes that the algebraic laws he proposes are satisfied if the basic symbols are interpreted as taking just the number values 0 and 1. Boole’s ideas have since undergone extensive development, and the resulting concept of Boolean algebra now plays a central role in mathematical logic, probability theory and computer design.

Hidden-Markov program algebra with iteration

We use hidden Markov models to motivate a quantitative compositional semantics for noninterference-based security with iteration, including a refinement- or ‘implements’ relation that compares two programs with respect to their information leakage; and we propose a program algebra for source-level reasoning about such programs, in particular as a means of establishing that an ‘implementation’ program leaks no more than its ‘specification’ program.This joins two themes: we extend our earlier work, having iteration but only qualitative (Morgan 2009), by making it quantitative; and we extend our earlier quantitative work (McIver et al. 2010) by including iteration.We advocate stepwise refinement and source-level program algebra – both as conceptual reasoning tools and as targets for automated assistance. A selection of algebraic laws is given to support this view in the case of quantitative noninterference; and it is demonstrated on a simple iterated password-guessing attack.

The Tensor Product Based Analytic Solutions of Nonlinear Optimal Control Problem

In this paper, the attention is focused on the optimization of a particular class of nonlinear systems. The optimum linear solution is not the best one so the problem of determining a nonlinear state feedback optimal control law with quadratic performance index over infinite time horizon is considered. It isn't an easy task and the most discouraging obstacle is the resolution of the Hamilton-Jacobi equation. Thus our contribution, based on the use of the tensor product and its algebraic laws, provide analytic solutions of the studied optimal control problem. The polynomial state feedback solution is computed through a numerical procedure. A numerical example is treated to illustrate the proposed solutions and some conclusions are drawn.