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Neural Computing and Applications

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23.05.2017 | Original Article

A finite-time convergent Zhang neural network and its application to real-time matrix square root finding

verfasst von: Lin Xiao

Erschienen in: Neural Computing and Applications | Sonderheft 2/2019

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Abstract

In this paper, a finite-time convergent Zhang neural network (ZNN) is proposed and studied for matrix square root finding. Compared to the original ZNN (OZNN) model, the finite-time convergent ZNN (FTCZNN) model fully utilizes a nonlinearly activated sign-bi-power function, and thus possesses faster convergence ability. In addition, the upper bound of convergence time for the FTCZNN model is theoretically derived and estimated by solving differential inequalities. Simulative comparisons are further conducted between the OZNN model and the FTCZNN model under the same conditions. The results validate the effectiveness and superiority of the FTCZNN model for matrix square root finding.

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Titel: A finite-time convergent Zhang neural network and its application to real-time matrix square root finding
verfasst von: Lin Xiao
Publikationsdatum: 23.05.2017
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe Sonderheft 2/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-017-3010-z

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