完整的线性方程解系统使用奇异值分解方法(SVD)

C. C. Marzuki, A. Agustian, Dewi Hariati, Junitis Afmilda, Nurul Husna, P. Nanda
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引用次数: 2

摘要

线性方程组可以排列成AX = B矩阵方程。线性中的常数也可以包含模糊数及其在模糊数中的所有参数,称为全模糊线性方程组。奇异值分解(SVD)是一种将a矩阵分解成USVH的三个分量的方法。SVD方法可用于求全模糊全线性方程组的解,该方程组也是不一致的全模糊线性方程组。用奇异值分解得到的一致的全模糊线性方程组的解是单解和多解。而对于不一致的全模糊线性方程组,用奇异值分解得到的解是最优逼近解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Penyelesaian Sistem Persamaan Linier Fully Fuzzy Menggunakan Metode Dekomposisi Nilai Singular (SVD)
Linear equation system can be arranged into the AX = B matrix equation. Constants in linear can also contain fuzzy numbers and all their parameters in fuzzy numbers known as fully fuzzy linear equation systems. singular value decomposition (SVD) is a method that decomposes an A matrix into three components of the USVH. The SVD method can be used to find a solution to the fully fuzzy fully linear equation system that is also an inconsistent fully fuzzy linear equation system. The solution obtained from a fully fuzzy linear equation system that is consistent using SVD is a single solution and many solutions. Whereas, the solution obtained from a fully fuzzy linear equation system that is inconsistent using SVD is the best approach solution.
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