Explore MATLAB source code curated for "优化" with clean implementations, documentation, and examples.
Implementation of PID parameter optimization using Particle Swarm Optimization (PSO) algorithm - This method has been published in a research paper demonstrating excellent control performance and convergence characteristics.
MATLAB source code implementation for optimizing RBF radial basis function neural networks using genetic algorithms, featuring complete algorithm workflow and parameter configuration.
The three-dimensional point set registration problem is a crucial challenge in computer technology. As a widely adopted algorithm for addressing this issue, the Iterative Closest Point (ICP) algorithm has garnered significant attention from researchers. This paper presents a novel approach to categorizing and summarizing various improvements and optimizations of the ICP algorithm for 3D point set registration, focusing on three key aspects: selection of registration elements, determination of registration strategies, and solution of error functions, with relevant algorithm implementations and key function descriptions.
A fundamental MATLAB fingerprint recognition implementation featuring filtering, image enhancement, binarization, and matching algorithms - designed for educational purposes with straightforward code structure.
Application Background: This program implements the Particle Swarm Optimization (PSO) algorithm to optimize Least Squares Support Vector Machine (LSSVM) parameters. Key Technologies: PSO heuristic optimization, LSSVM machine learning, hyperparameter tuning, classification and regression tasks.
MATLAB implementation of BP neural network optimization through genetic algorithms, featuring parameter tuning and structural enhancements with practical code examples
PSO Algorithm Toolbox for MATLAB: A comprehensive implementation enabling efficient optimization of multi-objective problems through swarm intelligence techniques, featuring configurable parameters and real-time convergence visualization.
This program implements genetic algorithm optimization for BP neural networks, featuring excellent performance and valuable learning content
I implemented a downloaded GA-PSO hybrid optimization algorithm that combines Genetic Algorithm and Particle Swarm Optimization strategies. The algorithm demonstrated good efficiency but required modifications to handle variable-specific boundary constraints. Key improvements include enabling independent upper/lower bounds for each design variable and fixing stability bugs through enhanced boundary-checking functions and constraint handling mechanisms.
This file contains MATLAB code implementing Particle Swarm Optimization (PSO) to optimize parameters for a cost-sensitive Support Vector Machine (SVM) model designed specifically for handling imbalanced datasets. The algorithm optimizes three key parameters: C1, C2, and sigma, using swarm intelligence principles for efficient parameter space exploration.