The fast convergence speed lowest complexity memoryless nonlinear gradient algorithm for a second-order adaptive IIR notch filter

Benjangkaprasert, Chawalit; Sirijiamrat, Supinya; Sangaroon, Ornlarp; Punchalard, Rachu; Janchitrapongvej, Kanok

The fast convergence speed lowest complexity memoryless nonlinear gradient algorithm for a second-order adaptive IIR notch filter

Author(s)

Benjangkaprasert, Chawalit

Sirijiamrat, Supinya

Sangaroon, Ornlarp

Punchalard, Rachu

Janchitrapongvej, Kanok

Date Issued

December 1, 2001

Type

Conference Paper

Abstract

The Fast convergence speed Lowest Complexity Memoryless Nonlinear Gradient algorithm for a second order adaptive IIR notch filter is presented in this paper. It is established based on a new Memoryless Nonlinear Gradient (MNG) algorithm and a Least Mean p-Power error criterion (LMP) algorithm with a vafue of p equal to unity. The proposed algorithm is very attractive due to their computational efficiencies and improved convergence properties. It is revealed by extensive simulations that it can produce significantly improved frequency estimates in both Gaussian and impulsive noise scenarios compared with the MNG and the other existing gradient-type algorithms. Several simulated results are provided to show the superiority of the proposed algorithm.

Citation

International Symposium on IC Technology Systems and Applications, 9, 319-322, 2001