Open AccessVarying‐parameter finite‐time zeroing neural network for solving linear algebraic systems
Author(s) -
Gerontitis Dimitrios,
Moysis L.,
Stanimirović Predrag,
Katsikis Vasilios N.,
Volos C.
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2019.4099
Subject(s) - artificial neural network , convergence (economics) , algebraic number , mathematics , term (time) , computer science , linear system , exponential function , function (biology) , control theory (sociology) , algorithm , artificial intelligence , mathematical analysis , control (management) , physics , quantum mechanics , evolutionary biology , economics , biology , economic growth
A new recurrent neural network is presented for solving linear algebraic systems with finite‐time convergence. The proposed model includes an exponential term in the Zhang neural network dynamical system, which leads to a faster convergence of the error‐monitoring function in comparison to previous methods. Theoretical analysis, as well as simulation results, validate the efficacy of the proposed model.