Recursive Least Squares Algorithm RLS for Volterra Kernel Estimation

The Recursive Least Squares (RLS) algorithm implements adaptive estimation of Volterra series kernels through recursive computation, suitable for dynamic system modeling and signal processing applications.

Recursive Algorithm for Least Squares Parameter Estimation

This implements a recursive least squares parameter estimation algorithm. The identified parameters after execution are: a1 = -1.4981, a2 = 0.7038, b1 = 1.0476, b2 = 0.4704. The algorithm processes data sequentially using a recursive update formula, making it suitable for real-time parameter identification and adaptive control applications.

Professional MATLAB source code with comprehensive documentation, examples, and detailed implementation guide.

MATLAB Code Implementation of Wiener Filter for Noise Reduction

MATLAB implementation of adaptive Wiener filter using recursive algorithms for denoising applications

Source Code Implementation of Recursive Maximum Likelihood Method for System Identification

MATLAB source code implementation for applying recursive maximum likelihood estimation in dynamic system parameter identification

System Parameter Identification Using Recursive Least Squares Algorithm

Online Parameter Estimation with Recursive Least Squares Method and M-Sequence Excitation

System Identification Examples with Code Implementation

Practical Examples of System Identification Methods with MATLAB Implementation Guidelines