Explore MATLAB source code curated for "BP神经网络" with clean implementations, documentation, and examples.
A MATLAB implementation of the Backpropagation Neural Network algorithm using a single hidden layer architecture, including an example file that performs classification on a dataset of 20 samples.
This project implements character recognition using neural networks, a classic pattern recognition application. The source code trains a Backpropagation (BP) neural network to recognize 26 English letters, demonstrating key implementation details including network architecture, training methodology, and feature extraction techniques.
Utilizing Genetic Algorithms to Optimize Neural Network Weights and Thresholds with Code Implementation
This project presents a comparative simulation study between Backpropagation (BP) and Radial Basis Function (RBF) neural networks. The package contains four MATLAB scripts that demonstrate different aspects of both network architectures, including training algorithms, performance evaluation, and practical applications.
Implementation Tutorial for Genetic Algorithm-Optimized BP Neural Network Algorithm For detailed explanations with code implementation examples, please refer to the included tutorial. Due to file size limitations, contact me for high-definition tutorials with complete MATLAB/Python code demonstrations.
A self-developed license plate recognition program implemented with BP neural network, demonstrating good performance. (Both images and program files are included in the same folder)
Implementing PSO-optimized BP neural networks for wind power forecasting with practical datasets and case studies, including code implementation insights for parameter optimization and neural network training.
Application Background Developed by Sun Chengyi et al. in 1998, the Mind Evolutionary Algorithm (MEA) serves as an effective optimization technique. This chapter details MEA's fundamental concepts and implements the algorithm in MATLAB through a nonlinear function fitting case study. Key Technologies 1. Training/Test Set Generation: Creating datasets using MATLAB's rand() and linspace() functions with proper data partitioning 2. Initial Population Initialization: Implementing population initialization with bounds checking using unifrnd() function 3. Subpopulation Convergence Operation: Performing crossover operations with tournament selection and simulated binary crossover (SBX) 4. Subpopulation Dissimilation Operation: Applying mutation operations using polynomial mutation with adaptive mutation rates 5. Optimal Individual Analysis: Implementing fitness evaluation and elite preservation techniques 6. BP Neural Network Training: Configuring network architecture with newff() and optimizing weights using MEA-based training 7. Simulation Testing and Result Analysis: Conducting performance evaluation with MSE metrics and convergence curve plotting
A MATLAB-implemented simple classifier using Error Backpropagation (BP) Neural Network with enhanced code-level insights.
Implementation of Genetic Algorithm-Optimized BP Neural Networks for Nonlinear System Fitting in MATLAB, combining evolutionary optimization principles with neural network training to enhance predictive performance.