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Numerical Algorithms
Published in: Numerical Algorithms 3/2020

06-12-2019 | Original Paper

New zeroing neural dynamics models for diagonalization of symmetric matrix stream

Authors: Yunong Zhang, Huanchang Huang, Min Yang, Yihong Ling, Jian Li, Binbin Qiu

Published in: Numerical Algorithms | Issue 3/2020

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Abstract

In this paper, the problem of diagonalizing a symmetric matrix stream (or say, time-varying matrix) is investigated. To fulfill our goal of diagonalization, two error functions are constructed. By making the error functions converge to zero with zeroing neural dynamics (ZND) design formulas, a continuous ZND model is established and its effectiveness is then substantiated by simulative results. Furthermore, a Zhang et al. discretization (ZeaD) formula with high precision is developed to discretize the continuous ZND model. Thus, a new 5-point discrete ZND (DZND) model is further proposed for diagonalization of matrix stream. Theoretical analyses prove the stability and convergence of the 5-point DZND model. In addition, simulative experiments are carried out, of which the results substantiate not only the efficacy of the proposed 5-point DZND model but also its higher computational precision as compared with the conventional Euler-type and 4-point DZND models for diagonalization of symmetric matrix stream.
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Appendix
Available only for authorised users
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Metadata
Title
New zeroing neural dynamics models for diagonalization of symmetric matrix stream
Authors
Yunong Zhang
Huanchang Huang
Min Yang
Yihong Ling
Jian Li
Binbin Qiu
Publication date
06-12-2019
Publisher
Springer US
Published in
Numerical Algorithms / Issue 3/2020
Print ISSN: 1017-1398
Electronic ISSN: 1572-9265
DOI
https://doi.org/10.1007/s11075-019-00840-5

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