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Newton's Method for Solving Non-Linear System of Algebraic Equations (NLSAEs) with MATLAB/Simulink and MAPLE

机译:用MATLAB / Simulink和MAPLE求解牛顿方程组(NLSAEs)非线性系统的方法

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Interest in Science, Technology, Engineering and Mathematics (STEM)-based courses at tertiary institution is on a steady decline. To curd this trend, among others, teaching and learning of STEM subjects must be made less mental tasking. This can be achieved by the aid of technical computing software. In this study, a novel approach to explaining and implementing Newton's method as a numerical approach for solving Nonlinear System of Algebraic Equations (NLSAEs) was presented using MATLAB and MAPLE in a complementary manner. Firstly, the analytical based computational software MAPLE was used to substitute the initial condition values into the NLSAEs and then evaluate them to get a constant value column vector. Secondly, MAPLE was used to obtain partial derivative of the NLSAEs hence, a Jacobean matrix. Substituting initial condition into the Jacobean matrix and evaluating resulted in a constant value square matrix. Both vector and matrix represent a Linear System of Algebraic Equations (LSAEs) for the related initial condition. This LSAEs was then solved using Gaussian Elimination method in the numerical-based computational software of MATLAB/Simulink. This process was repeated until the solution to the NLSAEs converged. To explain the concept of quadratic convergence of the Newton's method, power function of degree 2 (quad) relates the errors and successive errors in each iteration. This was achieved with the aid of Curve Fitting Toolbox of MATLAB. Finally, a script file and a function file in MATLAB were written that implements the complete solution process to the NLSAEs.
机译:大专院校对基于科学,技术,工程和数学(STEM)的课程的兴趣一直在下降。为了遏制这种趋势,除其他外,必须减少STEM主题的教学。这可以借助于技术计算软件来实现。在这项研究中,提出了一种新颖的方法来解释和实现牛顿方法,并将其作为求解非线性代数方程组(NLSAEs)的数值方法,并使用MATLAB和MAPLE进行了补充。首先,使用基于分析的计算软件MAPLE将初始条件值代入NLSAE,然后对其进行评估以获得恒定值列向量。其次,MAPLE用于获得NLSAE的偏导数,即雅可比矩阵。将初始条件代入Jacobean矩阵并进行评估,得出一个恒定值平方矩阵。向量和矩阵均表示相关初始条件的线性代数方程组(LSAE)。然后在基于数值的MATLAB / Simulink计算软件中使用高斯消除法求解该LSAE。重复该过程,直到对NLSAE的解收敛为止。为了解释牛顿法的二次收敛性的概念,阶次为2的幂函数(四次方)将每次迭代中的误差和连续误差联系起来。这是通过MATLAB的“曲线拟合工具箱”实现的。最后,在MATLAB中编写了一个脚本文件和一个功能文件,用于实现NLSAE的完整解决方案。

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