System Identification and Parameter Estimation

The provided source code implements system identification and parameter estimation methodologies, allowing users to estimate unknown system models from observed input-output data. The implementation includes algorithms that not only identify the model structure but also perform parameter estimation using techniques such as least squares estimation, maximum likelihood methods, or recursive algorithms for real-time applications. The code architecture supports making predictions based on the estimated parameters through simulation and validation functions. Additionally, the modular design allows for customization and adaptation to various model types, including linear ARX, ARMAX, state-space models, and nonlinear systems. Key functions include data preprocessing, model structure selection, parameter optimization, and model validation metrics calculation. For optimal performance, the estimation accuracy depends critically on data quality and appropriate algorithm selection. It is recommended to use well-conditioned, noise-reduced datasets and choose algorithms matching the system dynamics (e.g., using instrumental variables for noisy systems or subspace methods for MIMO systems). The code provides configuration parameters to adjust algorithm settings for specific applications, making it a versatile tool for researchers and engineers working in control systems, signal processing, and data analytics.