Statistical performance of the memoryless nonlinear gradient algorithm for the constrained adaptive IIR notch filter

@article{Xiao2005StatisticalPO,
  title={Statistical performance of the memoryless nonlinear gradient algorithm for the constrained adaptive IIR notch filter},
  author={Yegui Xiao and Liying Ma and Khashayar Khorasani and Akira Ikuta},
  journal={IEEE Transactions on Circuits and Systems I: Regular Papers},
  year={2005},
  volume={52},
  pages={1691-1702}
}
Gradient-type algorithms for the adaptive infinite-impulse response (IIR) notch filters are very attractive in terms of both performance and computational requirements for various real-life applications. This paper presents, in detail, a statistical analysis of the memoryless nonlinear gradient (MNG) algorithm applied to the well-known second-order adaptive IIR notch filter with constrained poles and zeros. This analysis is based on a proper use of Taylor series expansion and nonlinearization… CONTINUE READING
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