Professional MATLAB source code with comprehensive documentation and examples
A hybrid algorithm integrating genetic algorithms with membrane computing, leveraging membrane computing's distributed parallel processing capabilities combined with GA's evolutionary optimization
This program implements an adaptive control system using generalized fuzzy neural network theory developed with MATLAB programming, featuring enhanced algorithm descriptions and implementation details.
A self-developed particle swarm optimization implementation for solving the classic TSP problem, thoroughly tested with excellent performance results, ready for practical applications
Genetic Annealing Evolutionary Algorithm - A Hybrid Optimization Approach Combining Genetic Algorithms and Simulated Annealing
Random Forest is an ensemble classifier comprising multiple decision trees, where the final output class is determined by the majority vote of individual tree predictions. The implementation includes practical examples that can be executed to demonst
Genetic Algorithm-based Reactive Power Optimization with 21-Node Implementation Example - Fully Executable Code for Power System Analysis
MATLAB implementation of threshold segmentation using Mean Shift algorithm, which first clusters pixels with similar grayscale values through Mean Shift's segmentation characteristics, then applies threshold segmentation to separate foreground object
A comprehensive RBF neural network implementation designed for short-term load forecasting in power systems, featuring data preprocessing, network training, and prediction capabilities.
MATLAB source code for face recognition using Adaboost algorithm, featuring comprehensive face detection implementation with weak classifier combination and feature selection techniques
A fingerprint recognition program implemented in MATLAB that performs Gabor filtering, binarization, erosion and other preprocessing operations on fingerprint images, utilizing SVM algorithms to determine whether new fingerprint images exist in the o
Single-Objective Particle Swarm Optimization algorithm for parameter optimization and engineering applications, implemented with position-velocity updates and fitness evaluation mechanisms
MATLAB implementation of BRF neural network optimization using genetic algorithm, ready for direct prediction applications.
Developing predictive models based on Backpropagation Neural Networks through supervised learning with training datasets to forecast future outcomes
In statistical computing, the Expectation-Maximization (EM) algorithm is an iterative method for finding maximum likelihood (MLE) or maximum a posteriori (MAP) estimates of parameters in probabilistic models that depend on unobserved latent variables
This program provides source code for building neural networks based on wavelet functions, designed for analyzing ECG signals, EEG signals, and various biological signals with enhanced feature extraction capabilities.
Implementation of three neural network methods for handwritten character recognition - Probabilistic Neural Network (PNN), Radial Basis Function Network (RBF), and Backpropagation Neural Network (BP) with code-level insights
FCM Fuzzy C-Means Clustering Algorithm. Usage Instructions: This interactive FCM algorithm allows users to select a rectangular region using mouse interaction, after which the algorithm automatically applies clustering to the target area. The impleme
To assess the quality of clustering results, objective evaluation metrics are required to validate the rationality of clustering outcomes. Clustering performance evaluation methods are typically categorized into three types: external evaluation, inte
Source code for the fundamental Particle Swarm Optimization algorithm featuring constriction factor and inertia weight. This modularly structured code provides clear organization for easy enhancements and numerical experimentation, with key component
Establishing a BP neural network load forecasting model involves selecting appropriate network architecture (input layer, hidden layer, output layer) and optimizing wavelet neural network training functions to enhance convergence speed and prediction