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Murat Alper Basaran - One of the best experts on this subject based on the ideXlab platform.

  • calculating fuzzy inverse matrix using fuzzy Linear Equation system
    Applied Soft Computing, 2012
    Co-Authors: Murat Alper Basaran
    Abstract:

    Linear Equation systems play a very important role in engineering, mathematics, statistics and other disciplines. Fuzzifying either parameters or variables or both in these systems has been one of the research areas in the fuzzy literature since these kinds of systems are encountered in many applications. These systems are generally called fuzzy Linear Equations. Various types of these models have been examined for a decade. The solution procedures of these systems depend on different methods such as extension principle and interval arithmetic. Also, the method which is often used in computing inverse of a matrix in real case could be extended to fuzzy case, which employs Linear Equation system and identity matrix. For this purpose, we propose a new method which includes some new definitions which are fuzzy zero number, fuzzy one number and fuzzy identity matrix. Based on these definitions, direct computation of fuzzy inverse matrix is done using fuzzy arithmetic and fuzzy Equation system. Actually, this simply extends the notion used in real case to fuzzy case. Calculation is realized with two different settings. While the first one is called direct numerical solution, the other is obtained by choice of decision maker. It is noted that the uniqueness of the calculated fuzzy inverse matrix is not guaranteed.

Amit Kumar - One of the best experts on this subject based on the ideXlab platform.

  • Commentary on “Calculating fuzzy inverse matrix using fuzzy Linear Equation system”
    Applied Soft Computing Journal, 2017
    Co-Authors: Jagdeep Kaur, Amit Kumar
    Abstract:

    Basaran [Calculating fuzzy inverse matrix using fuzzy Linear Equation system, Applied Soft Computing, 12 (2012), 1810–1813] proposed a method for finding the inverse of a fuzzy matrix by assuming all the elements of the fuzzy inverse matrix as non-negative fuzzy numbers, while some of the elements of fuzzy matrix inverse may also be negative fuzzy numbers. Keeping the same in mind, Mosleh and Otadi [A discussion on “Calculating fuzzy inverse matrix using fuzzy Linear Equation system”, Applied Soft Computing, 28 (2015), 511–513] assumed (i, j) element x˜ij=(xij,αij,βijof the fuzzy inverse matrix as a non-negative fuzzy number if the value of xijobtained by Basaran's approach, is a non-negative real number and a negative fuzzy number if the value of xijis negative real number. In this paper, it is shown that the fuzzy multiplicative inverse of a fuzzy matrix, obtained by considering this assumption, is also not an exact fuzzy multiplicative inverse. Furthermore, the required modifications, in Mosleh and Otadi's approach, to obtain the exact multiplicative inverse of a fuzzy matrix are suggested.

Radu Slobodeanu - One of the best experts on this subject based on the ideXlab platform.

Angel Rincon - One of the best experts on this subject based on the ideXlab platform.

  • anisotropic strange quark stars with a non Linear Equation of state
    European Physical Journal Plus, 2019
    Co-Authors: Ilidio Lopes, Grigoris Panotopoulos, Angel Rincon
    Abstract:

    We obtain an exact analytical solution to Einstein’s field Equations assuming a non-Linear Equation-of-state and a particular mass function. Our solution describes the interior of anisotropic color flavor locked strange quark stars. All energy conditions are fulfilled, and therefore the solution obtained here is a realistic solution within General Relativity. The mass-to-radius profile is obtained, and the compactness of the star is computed.

  • electrically charged strange quark stars with a non Linear Equation of state
    European Physical Journal C, 2019
    Co-Authors: Grigoris Panotopoulos, Angel Rincon
    Abstract:

    The properties of electrically charged strange quark stars are investigated. We assume a non-Linear Equation-of-state, and we obtain numerical solutions to the structure Equations. The key features of the solutions obtained here are (i) they can support a 2 solar masses star, (ii) both the mass and the electric charge of the stars increase with the $$\alpha $$ parameter characterizing the electric density, (iii) the electric object is heavier and larger than its neutral counterpart.

Jagdeep Kaur - One of the best experts on this subject based on the ideXlab platform.

  • Commentary on “Calculating fuzzy inverse matrix using fuzzy Linear Equation system”
    Applied Soft Computing Journal, 2017
    Co-Authors: Jagdeep Kaur, Amit Kumar
    Abstract:

    Basaran [Calculating fuzzy inverse matrix using fuzzy Linear Equation system, Applied Soft Computing, 12 (2012), 1810–1813] proposed a method for finding the inverse of a fuzzy matrix by assuming all the elements of the fuzzy inverse matrix as non-negative fuzzy numbers, while some of the elements of fuzzy matrix inverse may also be negative fuzzy numbers. Keeping the same in mind, Mosleh and Otadi [A discussion on “Calculating fuzzy inverse matrix using fuzzy Linear Equation system”, Applied Soft Computing, 28 (2015), 511–513] assumed (i, j) element x˜ij=(xij,αij,βijof the fuzzy inverse matrix as a non-negative fuzzy number if the value of xijobtained by Basaran's approach, is a non-negative real number and a negative fuzzy number if the value of xijis negative real number. In this paper, it is shown that the fuzzy multiplicative inverse of a fuzzy matrix, obtained by considering this assumption, is also not an exact fuzzy multiplicative inverse. Furthermore, the required modifications, in Mosleh and Otadi's approach, to obtain the exact multiplicative inverse of a fuzzy matrix are suggested.