Explore MATLAB source code curated for "BP网络" with clean implementations, documentation, and examples.
A MATLAB example demonstrating Backpropagation Network implementation using Levenberg-Marquardt algorithm for enhanced training speed and superior performance in pattern recognition applications
Program implementation of a simple genetic algorithm and basic backpropagation neural network using MATLAB with detailed code structure and application examples
Implementing iris dataset classification using a manually coded BP neural network in MATLAB environment, featuring basic backpropagation implementation without utilizing built-in functions.
Implementing personal credit evaluation for bank clients using BP neural networks with approximately 70% accuracy rate
Implementing function approximation with backpropagation neural networks without using MATLAB's Neural Network Toolbox to understand BP network principles, algorithms, and manual coding techniques
The weight optimization of BP neural networks represents an unconstrained optimization problem with real-number encoding, making it suitable for direct implementation using MATLAB's genetic algorithm toolbox. The provided code demonstrates nonlinear regression for a system with 19 input variables and 1 output variable. For adaptation to different scenarios, users need only modify the encoding/decoding functions. The implementation requires the GAOT (Genetic Algorithm Optimization Toolbox) with enhancements including optimization algorithm selection, automated parameter tuning, and parallel computation capabilities.
This program implements a PID control system utilizing BP neural network technology. The system architecture combines two main components: a classical PID controller and a neural network module with code implementation for parameter optimization.
Implementation and computational procedure of Backpropagation Neural Network with code-level insights
Single Hidden Layer Backpropagation Network Routine with Clear Implementation Details
Using BP neural networks for cognitive radio spectrum detection and prediction, achieving efficient spectrum allocation through machine learning algorithms with backpropagation weight optimization