Explore MATLAB source code curated for "均方误差" with clean implementations, documentation, and examples.
This program simulates Bit Error Rate (BER) and Mean Square Error (MSE) under varying Signal-to-Noise Ratio (SNR) conditions using block-type pilots, with channel estimation implemented through both Least Squares (LS) method and an enhanced Least Squares approach.
Simulation of various OFDM channel estimation techniques including Least Squares (LS), Minimum Mean Square Error (MMSE), Linear Minimum Mean Square Error (LMMSE), and Singular Value Decomposition (SVD) methods. This graduation project material provides valuable reference for implementing channel estimation algorithms with practical code demonstrations.
Genetic Algorithm Implementation for TDOA Localization with Multiple Test Analysis - This project contains several core modules: program (main controller for multiple test iterations), definition_constant (parameter configuration), main_program (single trial execution), all_Noise (noise-corrupted TDOA calculation), and gen_ini_pop_arr (chromosome population initialization). The system performs account_test trials to find optimal chromosomes for each test while computing mean value (MV) and mean squared error (MSE) metrics.
MATLAB code for BP neural network algorithm that uses pre-trained network file ANN.mat to predict new data files, calculates mean squared error, and generates comparative plots between predicted and original data. Includes code explanations for data preprocessing, network loading, prediction implementation, and performance visualization.
Simulation of LS and MMSE channel estimation algorithms with mean square error as performance metric, including MATLAB implementation approaches and algorithm comparisons.
Implementation of image quality assessment metrics including Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Image Fidelity with MATLAB code examples. Uses fundamental programming approaches to help beginners understand objective image evaluation standards through practical implementation.
Estimating the frequency of a sinusoidal signal contaminated with additive white Gaussian noise via FFT involves computing the Fourier transform of x(n) to obtain the spectrum, identifying the frequency corresponding to the maximum magnitude, and calculating the mean squared error over multiple iterations. By varying the signal-to-noise ratio (SNR), simulations demonstrate that the mean squared error decreases as SNR increases, highlighting the method's robustness in noisy environments.
Image processing performance evaluation using metrics including Peak Signal-to-Noise Ratio (PSNR), Entropy, and Mean Square Error (MSE), with implementation approaches for calculating these quantitative measurements
Adaptive MMSE Equalizer with Algorithm Explanation and System Implementation
Implementation of spectrogram visualization along with signal-to-noise ratio (SNR) and mean square error (MSE) calculations for audio signal processing