  A new Lp-norm constraint least mean square (Lp-LMS) algorithm with new
strategy of varying p is presented, which is applied to system identification
in this letter. The parameter p is iteratively adjusted by the gradient method
applied to the root relative deviation of the estimated weight vector.
Numerical simulations show that this new algorithm achieves lower steady-state
error as well as equally fast convergence compared with the traditional Lp-LMS
and LMS algorithms in the application setting of sparse system identification
in the presence of noise.
