Explore MATLAB source code curated for "训练" with clean implementations, documentation, and examples.
Program framework for training and validating Elman RNN models applied to time series forecasting, featuring data preprocessing, network architecture configuration, and performance evaluation metrics
svmTrain Function - Support Vector Machine Training in MATLAB with Code Implementation Details
This MATLAB implementation demonstrates the use of neural network backpropagation method to train the correspondence relationship between infrared image pixel values and temperature, as presented in a research paper.
This BP Neural Network Simulink model example demonstrates a discrete transfer function implementation. The trained network can approximate arbitrary transfer functions or nonlinear functions through supervised learning algorithms.
A MATLAB-based face recognition program that implements a train-then-test approach, accepting an input image and returning the closest matching face images from the dataset.
Digit recognition system for classifying images of numbers 0 through 9, utilizing a dataset of 100 images with separate training and testing subsets for model validation and performance evaluation
Application Context: Suitable for scenarios with large existing datasets requiring comprehensive fitting and prediction analysis. Key Technology: Utilizes intelligent neural network control methods to perform data analysis and training on existing datasets, generating a composite error function. The trained model is then tested with validation data, producing comparative visualizations between predicted and actual results through appropriate plotting functions.
Digital speech recognition for numbers 0-9 with MATLAB implementation featuring both training and recognition capabilities
My custom implementation for training fuzzy neural networks with sampled data, including both datasets and program code with comprehensive data preprocessing and network training modules.
In the MATLAB environment, feature information extracted through various algorithms can be utilized as input for training Support Vector Machine (SVM) classifiers