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Design of IIR All-Pass Filters Using a Neural-Based Learning Algorithm

Authors
  • Chen, Li-Woei1
  • Jou, Yue-Dar2
  • Huang, Jian-Kai2
  • Hao, Shu-Sheng3
  • 1 R.O.C. Army Command Headquaters, Media Design Center, Long-Tan District, Taoyuan, 325, Taiwan, ROC , Taoyuan (Taiwan)
  • 2 R.O.C. Military Academy, Department of Electrical Engineering, Feng-Shan District, Kaohsiung, 830, Taiwan, ROC , Kaohsiung (Taiwan)
  • 3 National Defense University, School of Defense Science, Chung Cheng Institute of Technology, Ta-Hsi District, Taoyuan, 335, Taiwan, ROC , Taoyuan (Taiwan)
Type
Published Article
Journal
Circuits, Systems, and Signal Processing
Publisher
Springer US
Publication Date
Feb 17, 2015
Volume
34
Issue
9
Pages
3031–3056
Identifiers
DOI: 10.1007/s00034-015-9999-2
Source
Springer Nature
Keywords
License
Yellow

Abstract

Least-squares design of infinite impulse response all-pass filter can be formulated as an eigenvector solving problem based on the Rayleigh principle. The eigenfilter is designed by solving a single eigenvector corresponding to the smallest eigenvalue of a real, symmetric, and positive-definite matrix. This paper proposes a minor component analysis-based neural learning algorithm for designing eigenfilter. By appropriately mapping the associated all-pass filter specifications to the simple neural model enables the filter coefficients to be derived from the neural weights. The neural weights eventually approach the optimal filter coefficients of the eigenfilter when the neural model achieves convergence. The proposed neural learning algorithm is demonstrated from simulation results to converge rapidly and achieve accurate performance of eigenfilter design.

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