Least Squares Batch Processing Algorithm

This article discusses "System Identification," which refers to the process of modeling and analyzing systems to gain in-depth understanding of their behavior and performance. This process helps identify existing system issues and determine optimal solutions. For any system, identification is crucial as it enhances understanding of operational mechanisms, thereby improving efficiency and performance. Therefore, system identification is essential during system development or maintenance. From an implementation perspective, system identification typically involves collecting input-output data pairs and using algorithms like the least squares method to estimate model parameters. The core MATLAB function for batch least squares implementation is `arx`, which solves the normal equations through matrix operations like pseudoinverse calculation (`pinv`). Key steps include: data preprocessing (removing outliers), constructing regression matrices, and validating models using metrics like Mean Squared Error (MSE). For nonlinear systems, iterative algorithms such as Gauss-Newton optimization may be incorporated.