Explore MATLAB source code curated for "BP神经网络" with clean implementations, documentation, and examples.
A MATLAB-based program that trains BP neural networks using Particle Swarm Optimization (PSO) algorithm, featuring simple and practical code implementation with enhanced training efficiency.
Implementation of data classification through BP neural network using MATLAB programming approach
Modeling nonlinear systems using 1500 datasets for network training and 500 datasets for testing. Since BP neural networks typically initialize weights and thresholds randomly, they often get trapped in local minima. This method employs genetic algorithm optimization for initial weights and thresholds, with comparative analysis of pre- and post-optimization performance. Implementation includes population initialization, fitness function design based on MSE, and chromosome encoding of network parameters.
A MATLAB-based handwritten digit recognition program using Backpropagation (BP) Neural Network algorithm for image classification and digit identification.
MATLAB source code example for designing a classifier based on BP neural network, including implementation details and algorithm explanations.
Study on motor rotor fault diagnosis based on BP neural network, utilizing the trainbpx training function for neural network optimization and pattern recognition.
Face recognition, as a complex pattern recognition problem, has garnered widespread attention in recent years, with various methods in the recognition field demonstrating their strengths and leading to the development of many novel approaches that significantly enrich and broaden the direction of pattern recognition. This project utilizes an image database containing facial images from different angles, comprising 10 individuals with 5 images each, depicting face orientations: left, left-front, front, right-front, and right. An LVQ neural network is created to predict and recognize the orientation of any given facial image. Compared to BP neural networks, LVQ networks require no data preprocessing and directly compute distances between input vectors and competitive layers for pattern recognition. Recent years have seen increasingly in-depth research on LVQ neural networks, with applications becoming more widespread.
This implementation employs a three-layer BP neural network with optimized architecture using empirical formulas for hidden layer node calculation and parameter tuning, achieving robust handwritten digit recognition through systematic training and image preprocessing techniques.
MATLAB implementation of BP neural network for function approximation and pattern classification tasks
"BP.m" contains the complete source code for the BP neural network model; "train.fig" shows the final training visualization; "population_data_original.fig" displays prediction result graphs; "matlab_command_window_output.txt" captures console messages during execution; "generated_data.mat" stores post-execution data files. .bmp files serve the same purpose as .fig files.