Extended Infomax Algorithm
The Extended Infomax Algorithm featuring enhanced convergence speed and concise, transparent code implementation.
Image Denoising Using the Split Bregman Algorithm
Implementation of image denoising via the Split Bregman algorithm with rapid convergence and edge-preserving characteristics
Local Model of Particle Swarm Optimization Algorithm
Implementation of PSO's local model demonstrates superior performance compared to global model variants, featuring more reliable convergence to optimal solutions despite slower convergence rates, with key implementations including neighborhood topology management and local best position tracking.
MATLAB Implementation of Bat Algorithm with Performance Validation
Implementation of bat algorithm in MATLAB, utilizing several common benchmark test functions to evaluate convergence speed and solution accuracy.
A Globally Convergent Particle Swarm Optimization Algorithm with Significantly Improved Convergence Speed
An enhanced particle swarm optimization algorithm featuring guaranteed global convergence and substantially accelerated convergence rates through novel search strategies and adaptive parameter mechanisms
Step Size Adjustment Principle for Variable Step Size Adaptive Filtering Algorithm
This program proposes the step size adjustment principle for variable step size adaptive filtering algorithms: during the initial convergence phase or when unknown system parameters change, the step size should be relatively large to achieve faster convergence speed and better tracking capability for time-varying systems. After algorithm convergence, regardless of the magnitude of the interference signal v(n) at the primary input, a very small adjustment step size should be maintained to achieve minimal steady-state misadjustment noise. The program, developed based on the variable step size formula, provides significant reference value for implementing adaptive filtering systems with optimized convergence and stability characteristics.
Blind Equalization Constant Modulus Algorithm for Channel Equalization
A blind equalization constant modulus algorithm for channel equalization, featuring effective ISI suppression and rapid convergence speed with adaptive weight optimization.
Solving Function Optimization Using Genetic Algorithms
Implementing genetic algorithms for function optimization with fast convergence and minimal local optima entrapment. This classic algorithm is beginner-friendly, featuring clear code structure with key components like population initialization, fitness evaluation, crossover, and mutation operations.
Shu-Chuan Chu's Inspiration from Feline Daily Behaviors Leads to Cat Swarm Optimization Algorithm
Shu-Chuan Chu drew inspiration from cats' daily behavioral patterns to propose the Cat Swarm Optimization (CSO) algorithm in 2006. The algorithm's key feature lies in its simultaneous execution of local and global searches during evolutionary processes, achieving remarkable convergence speed through a unique dual-mode search mechanism.
A Particle Swarm Optimization Algorithm with Global Convergence and Significantly Improved Convergence Rate
A particle swarm optimization algorithm that achieves global convergence and substantially enhanced convergence speed.