Offline Identification Program Based on MATLAB System Identification Toolbox

The MATLAB System Identification Toolbox serves as a crucial tool in engineering fields for mathematical model development, particularly suited for dynamic system analysis. Its offline identification capability, as one of the core functions, enables users to complete model fitting based on pre-collected input-output data without requiring real-time data streams.

A typical offline identification workflow consists of three main steps: first, data preprocessing (including operations like noise removal and timestamp alignment using functions such as `detrend` or `resample`); second, model structure selection (common choices include ARX, state-space models with configurable orders); and finally, parameter calculation through optimization algorithms (least squares methods implemented in `arx`, or maximum likelihood estimation in `pem`). The toolbox's built-in `iddata` object efficiently packages data with proper domain specification, while functions like `tfest` (for transfer function estimation) or `ssest` (for state-space estimation) automatically fit corresponding models with optimal parameter tuning.

The key advantage lies in supporting multiple model validation techniques, including residual analysis through `resid` function and simulation comparison using `compare`. For nonlinear systems, the toolbox extends functionality through modular structures like Hammerstein-Wiener models implemented via `nlhw`. This solution is widely applied in industrial equipment modeling and academic research, though users should be cautious about overfitting issues and recommended to assess generalization capability through cross-validation methods like `crossval`.