RLS Algorithm Implementation for Adaptive Parameter Estimation
Implementation of RLS algorithm for adaptive parameter estimation of the system y(n) = 1.5*y(n-1)-0.7*y(n-2)+0.3*u(n-2)
Adaptive Equalization Using LMS and RLS Algorithms
Implementation of adaptive equalization procedures based on LMS and RLS algorithms, supporting various channel models including additive Gaussian channels, Rayleigh flat fading channels, and frequency-selective fading channels with MATLAB code examples and performance analysis.
LMS Algorithm and RLS Algorithm with Comparative Analysis
Detailed explanation of LMS and RLS adaptive filtering algorithms with comprehensive comparative analysis and annotated code implementation insights
Data Prediction Using RLS Algorithm and MATLAB Implementation
The Recursive Least Squares (RLS) algorithm, originally proposed by the renowned mathematician Gauss in 1795, represents a classical data processing methodology. Gauss established that when inferring unknown parameters from observed data, the most probable values are those that minimize the sum of squared differences between actual observations and calculated values, weighted by their precision measures - this forms the foundation of the famous least squares method. Widely applied in adaptive signal filtering analysis, the RLS algorithm offers rapid convergence and insensitivity to eigenvalue dispersion in autocorrelation matrices. However, it demands substantial computational resources. This chapter focuses on RLS-based data prediction techniques and their practical MATLAB implementation, including key algorithmic components and code optimization strategies.
Performance Comparison of LMS and RLS Algorithms
Performance comparison between LMS and RLS adaptive filtering algorithms, including weight convergence analysis, effects of forgetting factors, and MATLAB implementation considerations
RLS Algorithm Multi-Microphone Speech Denoising with Least Squares Adaptive Filtering
The RLS algorithm for multi-microphone speech denoising utilizes least squares adaptive filtering techniques, including comprehensive documentation and MATLAB code implementation.
MATLAB Implementation of Adaptive Equalizer Using RLS Algorithm
MATLAB program implementing an adaptive equalizer with Recursive Least Squares (RLS) algorithm, featuring comprehensive code implementation details and practical applications.
MATLAB Implementation of Adaptive Filter Using RLS Algorithm
MATLAB program implementing an adaptive filter based on the Recursive Least Squares (RLS) algorithm with signal processing applications
LMS Algorithm and RLS Algorithm for Adaptive Filtering
Implementation of LMS and RLS algorithms for adaptive filtering of random signals through a given system h, using tap weights w for system identification and inverse identification, while generating Mean Square Error (MSE) to evaluate signal recovery performance.
RLS Adaptive Linear Prediction Algorithm in Adaptive Signal Processing
Implementation of RLS (Recursive Least Squares) adaptive linear prediction algorithm for adaptive signal processing with code-level optimization