IEICE TRANSACTIONS on Fundamentals
High-Resolution Bearing Estimation via UNItary Decomposition Artificial Neural Network (UNIDANN)
Summary :
This paper describes a new Artificial Neural Network (ANN), UNItary Decomposition ANN (UNIDANN), which can perform the unitary eigendecomposition of the synaptic weight matrix. It is shown both analytically and quantitatively that if the synaptic weight matrix is Hermitian positive definite, the neural output, based on the proposed dynamic equation, will converge to the principal eigenvectors of the synaptic weight matrix. Compared with previous works, the UNIDANN possesses several advantageous features such as low computation time and no synchronization problem due to the underlying analog circuit structure, faster convergence speed, accurate final results, and numerical stability. Some simulations with a particular emphasis on the applications to high resolution bearing estimation problems are also furnished to justify the proposed ANN.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E81-A No.11 pp.2455-2462
- Publication Date
- 1998/11/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Category
- Neural Networks
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Shun-Hsyung CHANG, Tong-Yao LEE, Wen-Hsien FANG, "High-Resolution Bearing Estimation via UNItary Decomposition Artificial Neural Network (UNIDANN)" in IEICE TRANSACTIONS on Fundamentals,
vol. E81-A, no. 11, pp. 2455-2462, November 1998, doi: .
Abstract: This paper describes a new Artificial Neural Network (ANN), UNItary Decomposition ANN (UNIDANN), which can perform the unitary eigendecomposition of the synaptic weight matrix. It is shown both analytically and quantitatively that if the synaptic weight matrix is Hermitian positive definite, the neural output, based on the proposed dynamic equation, will converge to the principal eigenvectors of the synaptic weight matrix. Compared with previous works, the UNIDANN possesses several advantageous features such as low computation time and no synchronization problem due to the underlying analog circuit structure, faster convergence speed, accurate final results, and numerical stability. Some simulations with a particular emphasis on the applications to high resolution bearing estimation problems are also furnished to justify the proposed ANN.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e81-a_11_2455/_p
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@ARTICLE{e81-a_11_2455,
author={Shun-Hsyung CHANG, Tong-Yao LEE, Wen-Hsien FANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={High-Resolution Bearing Estimation via UNItary Decomposition Artificial Neural Network (UNIDANN)},
year={1998},
volume={E81-A},
number={11},
pages={2455-2462},
abstract={This paper describes a new Artificial Neural Network (ANN), UNItary Decomposition ANN (UNIDANN), which can perform the unitary eigendecomposition of the synaptic weight matrix. It is shown both analytically and quantitatively that if the synaptic weight matrix is Hermitian positive definite, the neural output, based on the proposed dynamic equation, will converge to the principal eigenvectors of the synaptic weight matrix. Compared with previous works, the UNIDANN possesses several advantageous features such as low computation time and no synchronization problem due to the underlying analog circuit structure, faster convergence speed, accurate final results, and numerical stability. Some simulations with a particular emphasis on the applications to high resolution bearing estimation problems are also furnished to justify the proposed ANN.},
keywords={},
doi={},
ISSN={},
month={November},}
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TY - JOUR
TI - High-Resolution Bearing Estimation via UNItary Decomposition Artificial Neural Network (UNIDANN)
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2455
EP - 2462
AU - Shun-Hsyung CHANG
AU - Tong-Yao LEE
AU - Wen-Hsien FANG
PY - 1998
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E81-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 1998
AB - This paper describes a new Artificial Neural Network (ANN), UNItary Decomposition ANN (UNIDANN), which can perform the unitary eigendecomposition of the synaptic weight matrix. It is shown both analytically and quantitatively that if the synaptic weight matrix is Hermitian positive definite, the neural output, based on the proposed dynamic equation, will converge to the principal eigenvectors of the synaptic weight matrix. Compared with previous works, the UNIDANN possesses several advantageous features such as low computation time and no synchronization problem due to the underlying analog circuit structure, faster convergence speed, accurate final results, and numerical stability. Some simulations with a particular emphasis on the applications to high resolution bearing estimation problems are also furnished to justify the proposed ANN.
ER -
English
