Application in System Identification: White Noise Generation

In system identification, MATLAB serves as a widely utilized tool that facilitates white noise generation and analysis. Below demonstrates a sample source code implementation for generating white noise, accompanied by execution results including corresponding graphical visualizations. The code utilizes MATLAB's built-in randn function, which generates normally distributed random numbers with zero mean and unit variance - fundamental properties of Gaussian white noise. The implementation involves specifying the number of data points (n=1000) to create a discrete white noise sequence suitable for system identification experiments. % White noise generation source code n = 1000; % Generate white noise with 1000 data points x = randn(n, 1); % Create Gaussian random numbers with zero mean and unit variance plot(x); % Visualize white noise sequence through time-domain plot Executing this code produces a white noise sequence and displays its temporal characteristics through the plotted graph. This functionality proves particularly valuable in system identification applications for generating excitation signals and analyzing system responses under stochastic inputs. The visualization enables immediate inspection of noise properties and signal behavior critical for identification algorithms.