Signal Recovery Using Constant Modulus Algorithm
CMA for Blind Channel Equalization and Estimation, Convergence Analysis of Constant Modulus Algorithm with Code Implementation Insights
SiRT Iterative Algorithm
Application Background: This algorithm implements an iterative procedure for solving large sparse systems of equations, demonstrating good convergence properties. While iteration time becomes longer for extremely large systems - a notable limitation - the method significantly improves computational efficiency for solving linear equations and delivers accurate solutions. Key Technology: The SiRT algorithm provides an efficient iterative approach for large sparse linear systems with robust convergence characteristics. Although computational time increases with system size, it remains a practical tool that produces reliable numerical solutions while enhancing overall solving capabilities.
Wireless Cellular Network Positioning Algorithm Program Enhanced with Scaling Factor
This innovative self-developed program modifies the Chan algorithm by incorporating a scaling factor, demonstrating excellent convergence and improved resistance to Non-Line-of-Sight (NLOS) propagation effects.
Immune Genetic Algorithm for Global Optimal Solution Search
The Immune Genetic Algorithm searches for global optimal solutions with verified high efficiency and fast convergence properties, implementing evolutionary mechanisms through code-level operations like antibody cloning and mutation.
Multipath Simulation of LMS Algorithm Using Simulink
This project implements multipath simulation of the LMS (Least Mean Square) algorithm using Simulink, verifying that the LMS system maintains excellent convergence and tracking performance even in multipath environments. The simulation includes adaptive filter implementation, channel modeling, and performance analysis.
Quantum Particle Swarm Optimization Algorithm Code Implementation
Application Background: Particle Swarm Optimization (PSO) is a prominent swarm intelligence algorithm that has become a research hotspot in stochastic optimization. Quantum-behaved Particle Swarm Optimization (QPSO) introduces quantum mechanical principles to probabilistically enhance traditional PSO. Key Technology: By incorporating quantum behavior, QPSO achieves superior convergence compared to basic PSO, demonstrating excellent performance across various applications. Code implementation typically involves quantum state probability distributions for position updates and delta potential well models for particle trajectory control.
Improved LMS Algorithm - Normalized LMS Algorithm
This program demonstrates an enhanced version of the least mean squares (LMS) algorithm - the normalized LMS algorithm, which offers superior convergence properties compared to traditional LMS. The implementation includes comparative plots showing performance differences, with code-based analysis of key parameters like step size normalization and error calculation.
Adaptive Filter Convergence Using LMS Algorithm
MATLAB simulation of LMS algorithm-based adaptive filter convergence featuring code implementation with step-size parameter analysis and mean square error performance evaluation
Algorithms for Multi-Objective Optimization
Multi-Objective Optimization Algorithms
33-Node Distribution Network Power Flow Calculation
Power Flow Calculation for 33-Node Distribution Network