Professional MATLAB source code with comprehensive documentation and examples
Comprehensive solutions for the Traveling Salesman Problem using intelligent optimization algorithms including Ant Colony Optimization, Simulated Annealing, Genetic Algorithms, Neural Networks, and Tabu Search with code implementation insights.
Improved genetic algorithm implementation for optimizing two key parameters in PID control systems with enhanced coding methodology
Structured programming implementation with modular design for easy modification and adaptability
Implementation of neural network optimization using Ant Colony Algorithm. Features ANT_object_func_ant as the neural network-based objective function and Ant_ant_new as the main optimization program with swarm intelligence implementation.
Neural network-based PID control does not use neural networks to tune PID parameters; instead, it employs a neural network directly as the controller, adjusting PID parameters indirectly by training the neural network's weight coefficients through ba
Implementation of improved ant colony optimization algorithm for finding shortest path between two points, using MATLAB for simulation and performance evaluation with enhanced pheromone update mechanisms and parameter optimization techniques.
Implementation of clustering analysis using genetic algorithms, including core functions such as fitness evaluation, selection operators, and crossover operations with Python/Matlab code architecture explanations.
MATLAB implementation of multi-objective particle swarm optimization featuring two objective functions (f1 and f2) using a weighted sum approach for solution convergence
A palmprint recognition program utilizing Gabor filters for feature extraction and Support Vector Machine (SVM) for classification
This program features a standard Elman neural network implementation enhanced with Particle Swarm Optimization (PSO) algorithm for weight optimization, capable of generating graphical outputs with clear and understandable code structure.
Extreme Learning Machine (ELM) is an efficient and user-friendly learning algorithm for Single-hidden Layer Feedforward Neural Networks (SLFNs). Proposed by Associate Professor Guang-Bin Huang at Nanyang Technological University in 2006, ELM eliminat
Modified and functional Mean Shift tracking algorithm with enhanced code implementation details
Multi-objective optimization involves two or more competing objectives under constraints, where optimizing one objective often sacrifices others, resulting in multiple non-dominated optimal solutions known as Pareto optimal solutions. The Fast Nondom
Implementation of a spectral clustering algorithm with development of a comprehensive spectral clustering toolbox
Simulated Annealing is a commonly used numerical optimization algorithm, ideal for beginners with its user-friendly implementation and clear conceptual understanding.
Integrating genetic algorithms and simulated annealing algorithms to optimize threshold selection for image analysis applications, with implementation insights on hybrid optimization techniques.
Implementation of color image segmentation using MATLAB, primarily based on clustering algorithms with code-level explanations of key functions and implementation approaches.
A comprehensive guide to implementing Bayesian network learning and probabilistic inference using MATLAB with code implementation strategies.
Cluster Analysis with Code Implementation Approaches
While PID control is extensively utilized in industrial processes, determining optimal PID parameters remains a key challenge. This example provides a reference implementation using genetic algorithms for PID enthusiasts, demonstrating how evolutiona