MATLAB Implementation of Continuous Wavelet Transform
Comprehensive guide to continuous wavelet transform with practical implementation - includes ready-to-run MATLAB code using provided data.mat file for hands-on signal processing experience.
Neural Network Prediction Using Backpropagation Algorithm
Implementation of a neural network prediction system utilizing the backpropagation (BP) algorithm, designed for efficient data forecasting with adaptable configuration parameters
SVM Classification Source Code Implemented in MATLAB
MATLAB-based source code implementing Support Vector Machine (SVM) for feature extraction and data classification. Utilizes MATLAB's built-in SVM functions with customizable data types and parameter configurations. The implementation includes flexible data preprocessing and kernel function options suitable for various machine learning applications.
Latent Semantic Analysis (LSA) Algorithm for Text Semantic Analysis
Implementation of Latent Semantic Analysis (LSA) algorithm for text semantic analysis with detailed function documentation, principle explanation, and sample data. Compared to previous LSA versions, this release includes demo.m for enhanced visualization capabilities, featuring SVD decomposition implementation and term-document matrix processing functions to improve user experience.
Dual-Population Ant Colony Optimization for TSP
Dual-Population Ant Colony Algorithm for Traveling Salesman Problem (TSP). Includes data files: "30-city TSP problem data with optimal solution.mat", "75-city TSP problem data.mat", and "442-city TSP data with algorithm comparison.mat" for algorithm validation and performance benchmarking.
BP Neural Network for Load Forecasting
BP Neural Network applied to load forecasting and electricity price estimation, with detailed explanations of each component's function. Includes sample datasets and result visualization graphs that demonstrate practical implementation outcomes.
SVM Algorithm Implementation for Clustering and Classification Routines
Implementation of clustering and classification routines using SVM algorithm. Includes experimental datasets, execution results, and a classic reference paper titled "A New Fuzzy Cover Approach to Clustering". Code enhancements demonstrate feature scaling, kernel selection, and hyperparameter optimization techniques.
MATLAB Code Implementation of SVM Toolbox
MATLAB SVM toolbox implementation with excellent usability. Place data in the MATLAB folder, open MATLAB, and execute the sequence: 1. mex setup 2. y 3. 2 4. y to obtain classification accuracy and test result matrix using training and testing samples. The implementation involves compiling C/C++ extensions through mex configuration for optimized SVM computation.
Filtering Data from a MAT File Followed by Singular Value Decomposition Denoising
Filtering data from a MAT file followed by denoising using Singular Value Decomposition. The SVD denoising methodology references literature provided in the attachment, with enhanced descriptions of code implementation approaches and key algorithmic steps.
SVM Data Transformation and Preprocessing Methods
Methods for data transformation and normalization functions in SVM, including key preprocessing techniques and implementation approaches