Explore MATLAB source code curated for "最小二乘法" with clean implementations, documentation, and examples.
MIMO-OFDM channel estimation implementation with performance comparison between Least Squares (LS) and Linear Minimum Mean Square Error (LMMSE) estimation algorithms, including MATLAB code structure and key parameter considerations.
Custom-implemented least squares support vector machine prediction model with runnable code, seeking performance improvements and enhanced predictive capabilities.
Three-Parameter Weibull Least Squares Estimation algorithm featuring high computational efficiency and accurate parameter estimation
This program implements the least squares polynomial fitting algorithm with significant reference value, demonstrating practical implementation of mathematical optimization techniques.
This MATLAB program implements two distinct localization algorithms: standard least squares method and weighted least squares method, providing implementation details and practical applications.
Implementation of multiple linear regression with least squares estimation in MATLAB, including data import, model fitting, result analysis, and prediction techniques.
This MATLAB program implements ellipse curve fitting using the least squares method through generalized matrix eigenvalue and eigenvector computation. The input variables x and y represent the horizontal and vertical coordinates of sample points respectively. The resulting coefficient matrix contains parameters [a, b, c, d, e, f] for the elliptical equation aX² + bXY + cY² + dX + eY + f = 0.
Design of linear classifiers in pattern recognition, including MATLAB implementations of Perceptron, Least Squares Method, and Support Vector Machine algorithms with detailed code explanations and parameter optimization techniques.
Implementation of ordinary least squares and recursive least squares algorithms for parameter identification in second-order dynamic systems with code implementation insights
MATLAB simulation implementation of Least Squares (LS) and Total Least Squares (TLS) estimation methods. The observation data is generated with zero-mean, unit-variance Gaussian white noise for n=1,2,...,128 samples. The simulation tests TLS with AR order 4 to estimate AR parameters and sinusoidal frequency, then uses SVD-TLS for the same estimation. Key implementation aspects include: (1) Testing AR orders 4 and 6, (2) Executing SVD-TCS with unknown AR order, (3) Running simulations minimum 20 times for statistical reliability.