Abstract The least mean square error difference (LMS-ED) minimum criterion for
an adaptive chaotic noise canceller is proposed in this paper.
Different from traditional least mean square error minimum criterion
in which the error is uncorrelated with the input vector, the
proposed LMS-ED minimum criterion tries to minimize the correlation
between the error difference and input vector difference. The novel
adaptive LMS-ED algorithm is then derived to update the weights of
adaptive noise canceller. A comparison between cancelling
performances of adaptive least mean square (LMS), normalized LMS
(NLMS) and proposed LMS-ED algorithms is simulated by using three
kinds of chaotic noises. The simulation results clearly show that the
proposed algorithm outperforms the LMS and NLMS algorithms in
achieving small values of steady-state excess mean square error.
Moreover, the computational complexity of the proposed LMS-ED
algorithm is the same as that of the standard LMS algorithms.
Fund:Supported by the National Natural Science Foundation of China (grant No 60572027), the Program for New Century Excellent Talents in University of China (Grant No NCET-05- 0794), and the National Key Lab. of Anti-jamming Communication Foundation of University of Electronic Science and Technology of China (Grant Nos 51434110104QT2201 and 51435080104QT2201).