Two novel finite time convergent recurrent neural networks for tackling complex-valued systems of linear equation

DOI10.2298/FIL2015009DzbMath1499.68304OpenAlexW3146705320MaRDI QIDQ5082431

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Publication date: 16 June 2022

Published in: Filomat (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2298/fil2015009d

zbMATH Keywords

complex-valued systems of linear equationNRNN-IRN modelNRNN-SBP modelZhang recurrent neural network models

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Iterative numerical methods for linear systems (65F10)

Cites Work

• A new design formula exploited for accelerating Zhang neural network and its application to time-varying matrix inversion
• A family of iterative methods with accelerated convergence for restricted linear system of equations
• Two-parameter TSCSP method for solving complex symmetric system of linear equations
• Discrete-time noise-tolerant Zhang neural network for dynamic matrix pseudoinversion
• Nonlinear recurrent neural networks for finite-time solution of general time-varying linear matrix equations
• Design, verification and robotic application of a novel recurrent neural network for computing dynamic Sylvester equation

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