Explore MATLAB source code curated for "人工神经网络" with clean implementations, documentation, and examples.
This program implements short-term load forecasting through genetic algorithm optimization and artificial neural network modeling. The package contains detailed documentation that can be accessed by extracting the compressed archive, including code implementation specifics and algorithm configuration parameters.
Implementation of Artificial Immune Algorithm and Neural Network for Bearing Pattern Recognition and MATLAB-based Fault Diagnosis with Code Examples
Artificial Neural Network MATLAB Source Code: This MATLAB program implements artificial neural network functionality with core algorithms including forward propagation, backpropagation, and activation functions.
MATLAB implementation of a three-layer BP artificial neural network to solve the XOR problem, featuring adjustable network architecture with configurable hidden layer neurons and activation functions
A MATLAB-implemented face recognition approach combining Gabor wavelet transform for feature extraction and artificial neural networks for pattern classification, including complete code implementation and algorithmic details.
MATLAB-based artificial neural network example featuring image processing capabilities and comprehensive code annotations
Ready-to-run artificial neural network programs and training datasets after decompression
MATLAB source code for fetal ECG signal extraction based on RBF (Radial Basis Function), BP (Backpropagation), and ANFIS (Adaptive Neuro-Fuzzy Inference System) artificial neural networks with algorithm implementation details
An organized reference table of artificial Neural Network utility functions providing efficient querying capabilities and implementation guidance for developers and researchers
SVR (Support Vector Regression) is a supervised learning algorithm based on support vector machines, primarily used for regression tasks and data prediction. It employs kernel functions to handle non-linear relationships and demonstrates robustness against outliers through epsilon-insensitive loss functions.