Professional MATLAB source code with comprehensive documentation and examples
Course experiment on pattern recognition using artificial neural networks, focusing on perceptron model implementation and backpropagation algorithm training methodology
Handwritten digit recognition using BP neural network with BMP image training and recognition rate calculation. The implementation includes preprocessing BMP images into normalized pixel arrays, designing multi-layer neural network architecture with
Implementation of the SMO algorithm for Support Vector Machine (SVM) with MATLAB source code files, SVM PDF documentation, and source code Word documentation. The project includes complete working examples demonstrating optimization steps and kernel
Freight Volume Forecasting Based on Generalized Regression Neural Network
A feature-rich manifold learning algorithm toolkit including graphical demo file (demo.fig), featuring Laplacian Eigenmaps implementation, manifold regularization adjustment, SVM classification algorithms, and other essential components for machine l
MATLAB implementation of genetic algorithm for multi-objective optimization, including dual-objective function optimization with detailed code implementation strategies
The two-dimensional multi-density grid clustering algorithm performs grid-based clustering on 2D data using a multi-density neighborhood approach, which involves partitioning data points into grid cells and applying density-based similarity measures
This program has been tested and verified on MATLAB 2009a and 2012b versions. Some functions may not exist or have different calling formats in older versions, modifications can be made by referring to the corresponding version's help documentation.
BP Neural Network for predicting personal income information using adult.data as training dataset and adult.test as testing dataset, with implementation of backpropagation algorithm for model optimization.
Bayesian Classifier Design: Building foundational pattern recognition knowledge through practical implementation of Bayesian decision theory algorithms with code examples
Complete working code implementation for the stacked autoencoder exercise from Stanford's Deep Learning Tutorial, featuring handwritten digit recognition dataset integration and ready-to-execute neural network architecture
This MATLAB program for fuzzy neural network decoupling is highly valuable for AI enthusiasts, featuring implementable decoupling algorithms and neural network training procedures.
Application Background: This project implements handwritten character recognition using artificial neural networks, specifically targeting digits ranging from 0 to 9. Technical Approach: The solution employs backpropagation algorithm as the core lear
A comprehensive collection of commonly used Particle Swarm Optimization algorithms, including basic PSO, linearly varying PSO, constriction factor PSO, random weight PSO, and more - designed to facilitate learning and implementation
An article comparing SVM and AdaBoost algorithms, implementing both methods for target recognition with detailed performance evaluation and code implementation insights.
Comprehensive guide to genetic algorithm theory, MATLAB implementation using key functions like ga() and custom operations, and practical applications in engineering optimization, machine learning, and data mining
My primary focus is on research in image segmentation algorithms, implemented using neural networks. The segmentation program leverages deep learning techniques with key functions for feature extraction and pixel classification.
Particle Swarm Optimization (PSO) algorithm addresses objective function optimization problems by simulating collective intelligence through position and velocity updates with fitness evaluation mechanisms.
Hossein Alipoor, Iris dataset, neural networks, MLP (Multilayer Perceptron) implementation
The integration of Genetic Algorithm with BP Neural Network enables effective segmentation of both color and grayscale images, with optimized parameter tuning and adaptive learning capabilities.