Explore MATLAB source code curated for "训练" with clean implementations, documentation, and examples.
This implementation package enables you to create, train, and test Fuzzy ART and Fuzzy ARTMAP neural networks. Developed by Aaron Garre, the package includes core algorithms for pattern recognition and fuzzy inference systems with modular code architecture.
MATLAB source code for training Self-Organizing Map neural networks, featuring unsupervised learning algorithms for data clustering and feature extraction, with detailed implementation guidelines.
This is my graduation project implementation of DLDA featuring both training and recognition modules. The algorithm demonstrates exceptional speed and achieves high recognition accuracy through optimized deep learning architecture.
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 training data. The implementation typically involves using k-means clustering for center selection and least squares methods for weight optimization.
Source code implementation using Particle Swarm Optimization (PSO) to train artificial neural networks, featuring weight optimization and fitness function evaluation.
This program implements quantum-behaved particle swarm optimization to train support vector machines, with validation performed on the IRIS dataset to demonstrate method effectiveness
Backpropagation Neural Network Design and Training - Reference this source code to master MATLAB BP programming concepts, including implementation details and network architecture
A basic digit recognition system implemented with BP neural network training and GUI interface, featuring image processing and pattern recognition capabilities
Implementation of face recognition using Linear Discriminant Analysis (LDA), including data loading from the ORL database, dataset partitioning for training and testing phases, and evaluation of classification performance metrics.
Face detection algorithm utilizing AdaBoost methodology, featuring complete training pipeline with optimized data reading structure to reduce memory consumption