Professional MATLAB source code with comprehensive documentation and examples
Time series prediction using neural networks demonstrated through sunspot data and rotor fault signal analysis, featuring MATLAB programming approaches including data preprocessing, network architecture selection, and prediction accuracy evaluation
Camera calibration program based on standard genetic algorithm implementation with comprehensive README documentation
KMeans clustering with predetermined cluster number, employing multiple algorithm runs to identify optimal initial configurations through distance-based centroid optimization
Genetic Algorithm for feature selection in pattern classification, using the MNIST dataset as a practical example with detailed implementation routines and code explanations.
Implementation of Particle Swarm Optimization (PSO) for optimizing BP neural networks in MATLAB environment, featuring verified working code with comprehensive algorithm explanations and parameter tuning guidance
This MATLAB-based reactive power optimization program utilizes genetic algorithms to enhance power system performance, featuring standard implementation, customizable parameters, and comprehensive visualization tools for optimal energy management sol
Initializes cluster centers randomly based on preset cluster count, computes Euclidean distances for data similarity measurement, and generates final clustering results through iterative centroid updates.
Backpropagation Neural Networks for nonlinear fitting and system modeling. BP neural networks function as black-box systems that establish spatial mapping relationships through data training. They can be effectively implemented for neural network pre
Implementation of effective classification for folk music, guzheng (Chinese zither), rock, and pop music genres through BP neural network architecture with feature extraction and pattern recognition techniques
Implementation of standard Cellular Neural Networks for image edge detection using MATLAB, featuring simplified code structure for better understanding
K-means clustering algorithm with MATLAB code examples - a widely used efficient clustering method with broad applications
How to Develop MATLAB Programs Using Genetic Algorithm Toolbox Functions to Solve Real-World Optimization Problems
Application of Artificial Bee Colony Algorithm for Intelligent Optimization Written in MATLAB
This EM algorithm implementation specializes in parameter estimation for Gaussian Mixture Models (GMM), featuring iterative E-step and M-step operations for optimal parameter convergence.
Implementing face detection using the AdaBoost algorithm with a custom image dataset, including code implementation details for feature extraction and cascade classifier training.
A comprehensive implementation of RBF network approximation optimized by genetic algorithms, featuring parameter optimization techniques and neural network training approaches suitable for function approximation tasks.
Implement nonlinear fitting using a simple bivariate quadratic function with BP neural networks to approximate actual values. Ideal for beginners with MATLAB code implementation examples for training and prediction functions.
Explore classic algorithm programs including Markov Chain, Monte Carlo methods, Ant Colony Optimization, and more, with practical MATLAB implementations to efficiently solve complex problems.
Implementing image segmentation with fuzzy clustering methods yields excellent results, although it may involve slower processing speeds due to computational complexity.
RBF (Radial Basis Function) neural networks represent a significant neural network architecture where training occurs in two distinct phases: first, obtaining initial weights through clustering algorithms, and second, refining network weights using t