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

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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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Acknowledgments

The authors are grateful to the editor and anonymous reviewers for their valuable recommendations and constructive comments, which truly helped toward an effective presentation of the proposed paper. The work was supported by the Ministry of Science and Technology of Republic of China under research contracts NSC-101-2221-E\(-\)145-004 and MOST-103-2221-E\(-\)145-001.

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Authors and Affiliations

  1. Media Design Center, R.O.C. Army Command Headquaters, Long-Tan District, Taoyuan, 325, Taiwan, ROC

    Li-Woei Chen

  2. Department of Electrical Engineering, R.O.C. Military Academy, P. O. Box 90602-6, Feng-Shan District, Kaohsiung, 830, Taiwan, ROC

    Yue-Dar Jou & Jian-Kai Huang

  3. School of Defense Science, Chung Cheng Institute of Technology, National Defense University, Ta-Hsi District, Taoyuan, 335, Taiwan, ROC

    Shu-Sheng Hao

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  1. Li-Woei Chen
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  2. Yue-Dar Jou
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  4. Shu-Sheng Hao
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Correspondence to Yue-Dar Jou.

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Chen, LW., Jou, YD., Huang, JK. et al. Design of IIR All-Pass Filters Using a Neural-Based Learning Algorithm. Circuits Syst Signal Process 34, 3031–3056 (2015). https://doi.org/10.1007/s00034-015-9999-2

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  • DOI: https://doi.org/10.1007/s00034-015-9999-2

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