Explore MATLAB source code curated for "LMS算法" with clean implementations, documentation, and examples.
A comprehensive LMS algorithm implementation featuring input/output processing, error rate calculation, and adaptive filtering capabilities. Freely downloadable code includes MATLAB functions for coefficient adaptation and performance monitoring.
A comprehensive implementation of LMS algorithm training filter featuring adaptive filtering, equalization processing, and hard decision capabilities including three main modules: training sequence optimization, equalization filtering, and bit error rate calculation
Digital Signal Processing LMS (Least Mean Squares) algorithm processes the error between input signal and reference signal, displaying convergence characteristics with implementation insights
Implementation of LMS algorithm MATLAB simulation with adaptive beamforming capabilities, featuring weight vector initialization, error signal computation, and gradient-based weight updates.
Implementation of an adaptive filter with 11-tap FIR structure. (1) Performance analysis with varying noise variance σ² (2) LMS algorithm demonstration: single experiment error square convergence curve (training length=500) with filter coefficients; 20 independent experiments showing average convergence curve with different step size comparisons (3) RLS algorithm implementation and comparative analysis between LMS and RLS algorithms
Adaptive filter design methodology featuring LMS algorithm source code and comprehensive MATLAB simulation implementation, including weight update mechanisms and performance verification.
MATLAB simulation of LMS algorithm-based adaptive filter convergence featuring code implementation with step-size parameter analysis and mean square error performance evaluation
This program implements a plant identification system using the Normalized Least Mean Square (NLMS) algorithm with frequency-domain fast computation, based on the reference paper "Frequency Domain Fast Implementation of LMS Algorithm"
This paper addresses bit error issues in digital communication systems caused by inter-symbol interference (ISI) and channel additive noise. The project designs an adaptive equalizer/filter using the Least Mean Squares (LMS) algorithm and implements hardware realization through VHDL (VHSIC Hardware Description Language) and FPGA (Field-Programmable Gate Array) technology. The implementation includes key components like tap-weight adaptation, error calculation, and finite impulse response (FIR) filtering operations. This standard graduation thesis serves as a technical reference for implementing adaptive filtering systems with programmable logic devices.
10 comprehensive examples covering adaptive filtering, blind equalization, decorrelation LMS algorithm, training vs decision-directed modes, noise cancellation, and adaptive filter design with implementation insights