Explore MATLAB source code curated for "求解" with clean implementations, documentation, and examples.
This MATLAB implementation provides source code for logistics facility location problems solved via Particle Swarm Optimization (PSO), featuring concise and practical programming with swarm intelligence optimization techniques
A MATLAB toolbox for convex optimization that enables rapid solving of convex optimization problems with excellent performance, featuring efficient algorithms and practical implementation examples.
Conjugate Gradient Method for unconstrained optimization - this implementation requires knowledge of both the objective function and its gradient to solve optimization problems efficiently. The algorithm is particularly suitable for large-scale problems due to its iterative nature and memory efficiency.
Implementation of a Particle Swarm Optimization (PSO) algorithm for PID parameter optimization, achieving significant performance improvements in control systems
Automated meshing and coding implementation for a 2D simple perfect electric conductor square cylinder in MATLAB environment, including equation integration and field value calculation at all points
A wavelet neural network program designed for training and generalization applications, featuring robust algorithm implementation and performance optimization.
MATLAB implementation examples including: solving nonlinear differential equations, Wiener filter demonstration, LMS adaptive filtering, steepest descent algorithm, and Gabor filtering algorithm for image texture feature extraction with code descriptions and algorithmic explanations.
MATLAB program for solving the workspace of a 3-DOF parallel robot, implementing spherical coordinate segmentation method with space partitioning algorithms
Implementation of Linear Mixed Integer Programming Problem Solving with Branch and Bound Algorithm (MATLAB Code)
Main Function for Homography Matrix Calculation in Pinhole Camera Model - Testing the algorithm for homography matrix computation. The program workflow involves: 1) Loading two input images, 2) Reading four pairs of corresponding points, 3) Calling CalcH function to compute the homography matrix, 4) Selecting a point interactively using mouse in the first image, 5) Calling WarpH function to compute the corresponding point in the second image through homography transformation, 6) Displaying the final results with visualization.