RLS Adaptive Filter Program for System Identification

The RLS adaptive filter program is designed for system identification applications. This program accepts Gaussian white noise as input and contains comprehensive in-code comments, enabling immediate execution. In system identification, the RLS adaptive filter serves as a widely-used methodology that autonomously adjusts filter parameters based on input signal characteristics, facilitating system identification and model development. The core algorithm implements recursive updates of filter weights using a covariance matrix inversion approach with forgetting factor optimization. Key functions include real-time coefficient adaptation through matrix inversion lemma to reduce computational complexity. By utilizing this RLS adaptive filter program, researchers can gain deeper insights into system behaviors and establish foundations for further analysis and applications. Therefore, mastering the RLS adaptive filter program is essential for both academic research and practical implementations in the system identification domain.