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% Implementation of SSOR preconditioned conjugate gradient method for solving Ax=b % Input parameters: % A - positive definite matrix [n*n] % b - right-hand side vector % omega - SSOR preconditioning parameter (range: 0--2) % Times - maximum iteration count % errtol - error tolerance for termination condition % % Output parameters: % NewX - approximate solution x for equation Ax=b % avgerr - current average absolute error during computation % % This implementation efficiently handles large sparse matrices using symmetric successive over-relaxation preconditioning to accelerate convergence.

MATLAB 330 views Tagged
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Equation Root-Finding Methods % inv - Matrix inversion for linear systems % roots - Polynomial root calculation % fzero - Single-variable function root solver % fsolve - Nonlinear system solver % solve - Symbolic equation solution % *newton - Newton-Raphson method implementation

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Implementation of ellipse fitting using least squares method - fit_ellipse(x, y, axis_handle) processes coordinate points (x,y) based on general conic equation formulation. This robust algorithm effectively minimizes fitting errors through mathematical optimization.

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This guide demonstrates system estimation using ARX and ARMAX models for a transfer function 2(s+1)/(s²+2s+2), including discrete conversion at 100ms sampling frequency with na=nb=2, nc=1 parameters, and pole-zero analysis implementation.

MATLAB 236 views Tagged
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