Explore MATLAB source code curated for "工具箱" with clean implementations, documentation, and examples.
The NSGA2 source code represents one of the most effective multi-objective evolutionary algorithms with superior convergence properties, featuring a comprehensive toolbox that facilitates implementation of various multi-objective optimization algorithms through modular functions for non-dominated sorting, crowding distance calculation, and genetic operations.
ARMDA is a specialized MATLAB toolbox designed for mining association rules from transactional data, featuring efficient algorithms for market basket analysis and frequent pattern discovery
LDPC MATLAB Toolbox providing comprehensive LDPC encoding and decoding implementation with algorithm demonstrations and performance analysis tools
This Particle Swarm Optimization (PSO) toolbox encapsulates the core algorithm components, providing users with adjustable parameters for customization. Users simply need to define their objective function (minimization or maximization), specify variable bounds, set iteration parameters like maximum velocity (Max_V), and the toolbox handles the optimization process automatically with configurable swarm intelligence mechanisms.
NSGA2 Source Code - The Recognized Best-Performing Multi-Objective Evolutionary Algorithm Offering Superior Convergence, Featuring an Integrated Toolkit for Implementing Various Multi-Objective Optimization Methods
Implementation of image processing GUI programming using MATLAB toolbox with the following functionalities: 1) Image reading and saving 2) Brightness and contrast adjustment through interactive GUI 3) ROI selection using mouse interaction 4) Image scaling with nearest-neighbor and bilinear interpolation algorithms 5) Histogram statistics and equalization 6) Noise addition and filtering algorithms for denoising
MATLAB Dynamical Systems and Time Series Analysis Toolbox: This comprehensive toolbox enables analysis of dynamical systems and time series data, supporting customization for Ordinary Differential Equations (ODE) and Stochastic Differential Equations (SDE). All analytical methods are encapsulated within modular functions accessible via command-line interface or graphical user interface (GUI). Key functionalities include: ODE/SDE solvers, map integration for time series analysis, filtering techniques, normalization/standardization procedures, histogram/2D histogram visualization, Auto-Correlation Function (ACF), Mutual Information Index (MAI), Fast Fourier Transform (FFT), maximum Lyapunov exponent calculation, and pattern recognition algorithms. Dynamical system analysis features comprise Poincaré section generation, bifurcation diagram plotting, and Lyapunov exponent computation routines.
Implementation program for initial population generation in classical genetic algorithm toolbox with enhanced technical specifications
Contains comprehensive genetic algorithm implementations with detailed code examples, featuring optimization techniques, selection methods, and fitness evaluation functions to facilitate understanding of evolutionary computation principles.
PLS Algorithm Toolbox: Partial Least Squares Regression ≈ Multiple Linear Regression Analysis + Canonical Correlation Analysis + Principal Component Analysis