Solution of Fuzzy System of Linear Equations with Polynomial Parametric Form

This paper proposed two new and simple solution methods to solve a fuzzy system of linear equations having fuzzy coefficients and crisp variables using a polynomial parametric form of fuzzy numbers. Related theorems are stated and proved. The proposed methods are used to solve example problems. The results obtained are also compared with the known solutions and are found to be in good agreement

A modification of fuzzy arithmetic operators for solving near-zero fully fuzzy matrix equation

Matrix equations have its own important in the field of control system engineering particularly in the stability analysis of linear control systems and the reduction of nonlinear control system models. There are certain conditions where the classical matrix equation are not well equipped to handle the uncertainty problems such as during the process of stability analysis and reduction in control system engineering. In this study, an algorithm is developed for solving fully fuzzy matrix equation particularly for ~ A ~X ~B  ~X = ~ C, where the coefficients of the equation are in near-zero fuzzy numbers. By modifying the existing fuzzy multiplication arithmetic operators, the proposed algorithm exceeds the positive restriction to allow the near-zero fuzzy numbers as the coefficients. Besides that, a new fuzzy subtraction arithmetic operator has also been proposed as the existing operator failed to satisfy the both sides of the nearzero fully fuzzy matrix equation. Subsequently, Kronecker product and V ec-operator are adapted with the modified fuzzy arithmetic operator in order to transform the fully fuzzy matrix equation to a fully fuzzy linear system. On top of that, a new associated linear system is developed to obtain the final solution. A numerical example and the verification of the solution are presented to demonstrate the proposed algorithm

Fuzzy Symmetric Solutions of Fuzzy Matrix Equations

The fuzzy symmetric solution of fuzzy matrix equation A X = B, in which A is a crisp m × m nonsingular matrix and B is an m × n fuzzy numbers matrix with nonzero spreads, is investigated. The fuzzy matrix equation is converted to a fuzzy system of linear equations according to the Kronecker product of matrices. From solving the fuzzy linear system, three types of fuzzy symmetric solutions of the fuzzy matrix equation are derived. Finally, two examples are given to illustrate the proposed method

Transportation Management in a Distributed Logistic Consumption System Under Uncertainty Conditions

The problem of supply management in the supplier-to-consumer logistics transport system has been formed and solved. The novelty of the formulation of the problem consists in the integrated accounting of costs in the logistic system, which takes into account at the same time the cost of transporting products from suppliers to consumers, as well as the costs for each of the consumers to store the unsold product and losses due to possible shortages. The resulting optimization problem is no longer a standard linear programming problem. In addition, the work assumes that the solution of the problem should be sought taking into account the fact that the initial data of the problem are not deterministic. The analysis of traditional methods of describing the uncertainty of the source data. It is concluded that, given the rapidly changing conditions for the implementation of the delivery process in a distributed supplier-to-consumer system, it is advisable to move from a theoretical probability representation of the source data to their description in terms of fuzzy mathematics. At the same time, in particular, the fuzzy values of the demand for the delivered product for each consumer are determined by their membership functions.Distribution of supplies in the system is described by solving a mathematical programming problem with a nonlinear objective function and a set of linear constraints of the transport type. In forming the criterion, a technology is used to transform the membership functions of fuzzy parameters of the problem to its theoretical probabilistic counterparts – density distribution of demand values. The task is reduced to finding for each consumer the value of the ordered product, minimizing the average total cost of storing the unrealized product and losses from the deficit. The initial problem is reduced to solving a set of integral equations solved, in general, numerically. It is shown that in particular, important for practice, particular cases, this solution is achieved analytically.The paper states the insufficient adequacy of the traditionally used mathematical models for describing fuzzy parameters of the problem, in particular, the demand. Statistical processing of real data on demand shows that the parameters of the membership functions of the corresponding fuzzy numbers are themselves fuzzy numbers. Acceptable mathematical models of the corresponding fuzzy numbers are formulated in terms of bifuzzy mathematics. The relations describing the membership functions of the bifuzzy numbers are given. A formula is obtained for calculating the total losses to storage and from the deficit, taking into account the bifuzzy of demand. In this case, the initial task is reduced to finding the distribution of supplies, at which the maximum value of the total losses does not exceed the permissible value

Semi-analytical scheme for solving intuitionistic fuzzy system of differential equations

The aim of this article is to implement the Generalized Modified Adomian Decomposition Method to compute the semi-numerical solution of the linear system of intuitionistic fuzzy initial value problems. Here, we consider the initial values as generalized trapezoidal intuitionistic fuzzy numbers. The technique is applied to brine tanks problem and coupled mass spring systems.Theoretically, different approaches to solving a system of generalized trapezoidal intuitionistic fuzzy differential equations are discussed in this study under the presumption that the coefficients of the system of the differential equations are associated to generalized trapezoidal intuitionistic fuzzy numbers. The approximate results are compared with exact solutions which shows good efficiency. The corresponding graphs at different levels of uncertainty show the example’s numerical outcomes. The graphical representations further demonstrate the effectiveness and accuracy of the proposed method in comparison to existing semi-numerical methods in the literature

Numerical solution of interval and fuzzy system of linear equations

The system of linear equation has a great importance in many real life problem such as economics, Optimization and in various engineering field. We know that system of linear equations, in general is solved for crisp unknowns. For the sake of simplicity or for fuzzy computation it is taken as crisp value. In actual case the parameters of the system of linear equations are modeled by taking the experimental or observation data. So the parameters of the system actually contain uncertainty rather than the crisp one. The uncertainties may be considered in term of interval or fuzzy number. Recently different authors have investigated these problems by various methods. These methods are described for the system having various type of fuzzy and non-fuzzy parameters. Although solutions are obtained by these methods are good but sometimes the method requires lengthy procedure and computationally not efficient. Here, in this thesis detail study of linear simultaneous equations with interval and fuzzy parameter have been done. New methods have been proposed for the same. The proposed methods have been tested for known problems viz. a circuit analysis, solved in the literature and the results are found to be in good agreement with the present. Next more example problems are solved using the proposed methods to have confidence on these new methods. There exists various type of fuzzy numbers. As such here the problems are also been solved by two types of fuzzy numbers. Corresponding plots of the solution for all the example problems are included in this thesis. In view of the above analysis of the results, it is found that the proposed methods are simple and computationally efficient

Numerical solution of static and dynamic problems of imprecisely defined structural systems

Static and dynamic problems with deterministic structural parameters are well studied. In this regard, good number of investigations have been done by many authors. Usually, structural analysis depends upon the system parameters such as mass, geometry, material properties, external loads and boundary conditions which are defined exactly or considered as deterministic. But, rather than the deterministic or exact values we may have only the vague, imprecise and incomplete informations about the variables and parameters being a result of errors in measurements, observations, experiments, applying different operating conditions or it may be due to maintenance induced errors, etc. which are uncertain in nature. Hence, it is an important issue to model these types of uncertainties. Basically these may be modelled through a probabilistic, interval or fuzzy approach. Unfortunately, probabilistic methods may not be able to deliver reliable results at the required precision without sufficient experimental data. It may be due to the probability density functions involved in it. As such, in recent decades, interval analysis and fuzzy theory are becoming powerful tools. In these approaches, the uncertain variables and parameters are represented by interval and fuzzy numbers, vectors or matrices.In general, structural problems for uncertain static analysis with interval or fuzzy parameters simplify to interval or fuzzy system of linear equations whereas interval or fuzzy eigenvalue problem may be obtained for the dynamic analysis. Accordingly, this thesis develops new methods for finding the solution of fuzzy and interval system of linear equations and eigenvalue problems. Various methods based on fuzzy centre, radius, addition, subtraction, linear programming approach and double parametric form of fuzzy numbers have been proposed for the solution of system of linear equations with fuzzy parameters. An algorithm based on fuzzy centre has been proposed for solving the generalized fuzzy eigenvalue problem. Moreover, a fuzzy based iterative scheme with Taylor series expansion has been developed for the identification of structural parameters from uncertain dynamic data. Also, dynamic responses of fractionally damped discrete and continuous structural systems with crisp and fuzzy initial conditions have been obtained using homotopy perturbation method based on the proposed double parametric form of fuzzy numbers. Numerical examples and application problems are solved to demonstrate the efficiency and capabilities of the developed methods. In this regard, imprecisely defined structures such as bar, beam, truss, simplified bridge, rectangular sheet with fuzzy/interval material and geometric properties along with uncertain external forces have been considered for the static analysis. Fuzzy and interval finite element method have been applied to obtain the uncertain static responses. Structural problems viz. multistorey shear building, spring mass mechanical system and stepped beam structures with uncertain structural parameters have been considered for dynamic analysis. In the identification problem, column stiffnesses of a multistorey frame structure have been identified using uncertain dynamic data based on the proposed algorithm. In order to get the dynamic responses, a single degree of freedom fractionally damped spring-mass mechanical system and fractionally damped viscoelastic continuous beam with crisp and fuzzy initial conditions are also investigated.Obtained results are compared in special cases for the validation of proposed methods