Adaptive coefficient designs for nonlinear activation function and its application to zeroing neural network for solving time-varying Sylvester equation

DOI10.1016/j.jfranklin.2020.06.029zbMath1451.68226OpenAlexW3041535098MaRDI QIDQ2005405

Yanyan Li

Publication date: 7 October 2020

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2020.06.029

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Adaptive control/observation systems (93C40) Matrix equations and identities (15A24) Numerical methods for matrix equations (65F45)

Related Items (5)

A robust fast convergence zeroing neural network and its applications to dynamic Sylvester equation solving and robot trajectory tracking ⋮ Fixed-time convergence integral-enhanced ZNN for calculating complex-valued flow matrix Drazin inverse ⋮ A novel finite-time complex-valued zeoring neural network for solving time-varying complex-valued Sylvester equation ⋮ Nonlinear function activated GNN versus ZNN for online solution of general linear matrix equations ⋮ Finite-time convergent zeroing neural network for solving time-varying algebraic Riccati equations

Cites Work

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• Mittag‐Leffler state estimator design and synchronization analysis for fractional‐order BAM neural networks with time delays
• Algorithm 432 [C2: Solution of the matrix equation AX + XB = C [F4]]

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