Adaptive filters are useful solutions for system identification problem where an optimization problem is used to formulated the estimation of the unknown model coefficients. The nonnegativity constraint is one of the most frequently used constraint which can be imposed to avoid physically unreasonable solutions and to comply with physical characteristics. In this letter, we propose a new variant of non-negative least mean square (NNLMS) that has a less mean square error (MSE) value and faster convergence rate. We provide both mean weight behavior and transient excess mean-square error analysis for proposed algorithm. Simulation results validate the theoretical analysis and show the effectiveness of our proposed algorithm.
