Optimization of Parameters C and γ in Support Vector Machines
Optimizing SVM hyperparameters C and gamma with cross-validation and grid search techniques
Kalman Filter-based PID Controller Parameter Optimization
MATLAB implementation of PID controller parameter optimization using Kalman filter algorithm, designed to run in MATLAB environment with comprehensive code structure and algorithmic details
Bacterial Foraging Optimization Algorithm with Implementation for PI Controller Parameter Tuning
Implementation of Bacterial Foraging Optimization Algorithm for proportional-integral controller parameter optimization, featuring comprehensive code structure explanation and algorithmic workflow - ideal for beginners learning bio-inspired optimization techniques.
Basic Genetic Algorithm for Optimization of 4 Parameters
A fundamental genetic algorithm program designed for PID controller optimization, implementing parameter tuning through evolutionary computation with selection, crossover, and mutation operations
Optimization of Support Vector Machine Parameters C and G: Grid Search, Genetic Algorithm, and Particle Swarm Optimization
Comprehensive guide to optimizing SVM parameters C and G using three methods: Grid Search, Genetic Algorithm, and Particle Swarm Optimization, complete with algorithm explanations and implementation insights for practical learning.
Bacterial Foraging Optimization Algorithm
This optimization algorithm is designed for control parameter tuning, specifically for PID controller optimization and gain value adjustment through biologically-inspired computational methods.
PID Controller Parameter Optimization Using Ant Colony Algorithm
MATLAB source code for PID controller parameter optimization based on Ant Colony Algorithm, featuring detailed implementation steps with comprehensive documentation
BP Neural Network for PID Controller Parameter Optimization
Implementation of BP neural network for optimizing PID controller parameters, featuring a directly executable program with excellent optimization performance that enhances system stability and responsiveness.
Multi-Objective Particle Swarm Optimization Algorithm
Multi-Objective Particle Swarm Optimization (MOPSO) for parameter optimization and multi-objective problem solving
Particle Swarm Optimization for RBF Network Enhancement
This MATLAB implementation employs Particle Swarm Optimization (PSO) to optimize parameters in Radial Basis Function (RBF) neural networks, with testing demonstrating superior function approximation capabilities compared to non-optimized RBF networks through comprehensive benchmark validation.