A Harmonic Retrieval Algorithm with Neural Computation
Summary :
A novel harmonic retrieval algorithm is proposed in this paper based on Hopfield's neural network. Frequencies can be retrieved with high accuracy and high resolution under low signal to noise ratio (SNR). Amplitudes and phases in harmonic signals can also be estimated roughly by an energy constrained linear projection approach as proposed in the algorithm. Only no less than 2q neurons are necessary in order to detect harmonic siglnals with q different frequencies, where q denotes the number of different frequencies in harmonic signals. Experimental simulations show fast convergence and stable solution in spite of low signal to noise ratio can be obtained using the proposed algorithm.
- Publication
- IEICE TRANSACTIONS on Information Vol.E75-D No.5 pp.718-727
- Publication Date
- 1992/09/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Bio-Cybernetics
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Mingyoung ZHOU, Jiro OKAMOTO, Kazumi YAMASHITA, "A Harmonic Retrieval Algorithm with Neural Computation" in IEICE TRANSACTIONS on Information,
vol. E75-D, no. 5, pp. 718-727, September 1992, doi: .
Abstract: A novel harmonic retrieval algorithm is proposed in this paper based on Hopfield's neural network. Frequencies can be retrieved with high accuracy and high resolution under low signal to noise ratio (SNR). Amplitudes and phases in harmonic signals can also be estimated roughly by an energy constrained linear projection approach as proposed in the algorithm. Only no less than 2q neurons are necessary in order to detect harmonic siglnals with q different frequencies, where q denotes the number of different frequencies in harmonic signals. Experimental simulations show fast convergence and stable solution in spite of low signal to noise ratio can be obtained using the proposed algorithm.
URL: https://global.ieice.org/en_transactions/information/10.1587/e75-d_5_718/_p
Copy
@ARTICLE{e75-d_5_718,
author={Mingyoung ZHOU, Jiro OKAMOTO, Kazumi YAMASHITA, },
journal={IEICE TRANSACTIONS on Information},
title={A Harmonic Retrieval Algorithm with Neural Computation},
year={1992},
volume={E75-D},
number={5},
pages={718-727},
abstract={A novel harmonic retrieval algorithm is proposed in this paper based on Hopfield's neural network. Frequencies can be retrieved with high accuracy and high resolution under low signal to noise ratio (SNR). Amplitudes and phases in harmonic signals can also be estimated roughly by an energy constrained linear projection approach as proposed in the algorithm. Only no less than 2q neurons are necessary in order to detect harmonic siglnals with q different frequencies, where q denotes the number of different frequencies in harmonic signals. Experimental simulations show fast convergence and stable solution in spite of low signal to noise ratio can be obtained using the proposed algorithm.},
keywords={},
doi={},
ISSN={},
month={September},}
Copy
TY - JOUR
TI - A Harmonic Retrieval Algorithm with Neural Computation
T2 - IEICE TRANSACTIONS on Information
SP - 718
EP - 727
AU - Mingyoung ZHOU
AU - Jiro OKAMOTO
AU - Kazumi YAMASHITA
PY - 1992
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E75-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - September 1992
AB - A novel harmonic retrieval algorithm is proposed in this paper based on Hopfield's neural network. Frequencies can be retrieved with high accuracy and high resolution under low signal to noise ratio (SNR). Amplitudes and phases in harmonic signals can also be estimated roughly by an energy constrained linear projection approach as proposed in the algorithm. Only no less than 2q neurons are necessary in order to detect harmonic siglnals with q different frequencies, where q denotes the number of different frequencies in harmonic signals. Experimental simulations show fast convergence and stable solution in spite of low signal to noise ratio can be obtained using the proposed algorithm.
ER -
English
