Recursive Least Squares (RLS) Algorithm
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This article explores MATLAB simulation of the Recursive Least Squares (RLS) algorithm, a powerful mathematical method for finding optimal solutions in large datasets. We delve deep into the algorithm's mathematical foundations, implementation techniques in MATLAB, and practical performance evaluation. The implementation typically involves initializing parameters like the forgetting factor and covariance matrix, then iteratively updating weight estimates using matrix operations. Key functions include computing the gain vector and updating the inverse covariance matrix through the Woodbury identity. Additionally, we examine the algorithm's advantages (fast convergence, tracking capability) and limitations (computational complexity), along with optimization strategies such as adaptive forgetting factors and regularization techniques for improved stability across different application scenarios. Join us in exploring this versatile adaptive filtering algorithm!
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