Modified LMS and NLMS algorithms with non-negative weights - Bershad, Neil J.; Bermudez, José C.M. (Signal processing, 2024)
Non-negative constraints arise in certain system identification problems when the systems to be identified have only positive coefficients. This paper studies the stochastic behavior of modified LMS and NLMS algorithms, modified so as to only allow adaptation with positive coefficients. The mean and second moment behavior of these algorithms are analyzed for Gaussian inputs. Excellent agreement is demonstrated between the theory and Monte Carlo simulations for the LMS algorithm. •Convergence of LMS and NLMS algorithms under non-negative weight constraints.•At each iteration, the weight vector is projected onto the feasible space.•The new algorithm optimally projects the weight vector onto the feasible set.•Models are derived for the mean and mean-square behavior of the adaptive weights.•Simulation results are in excellent agreement with theoretical predictions.