MATLAB Simulation Source Code for Model Identification

In this document, we explore MATLAB simulation source code for model identification. We introduce two modeling approaches: least squares method and maximum likelihood estimation method. The implementation includes core functions such as lsqcurvefit for nonlinear least squares optimization and mle for maximum likelihood parameter estimation. Additionally, we provide detailed explanations for each key code example to help readers better understand the implementation and application of these methods. The code demonstrates practical considerations like handling measurement noise and validating model accuracy through residual analysis. Furthermore, we discuss the advantages and disadvantages of these methods in practical applications, along with guidelines for selecting the optimal modeling approach based on different application scenarios. This document aims to provide valuable reference material to help readers better understand and apply MATLAB simulation source code for model identification.