Design of a nonlinearly activated gradient-based neural network and its application to matrix inversion

DOI10.2298/FIL2015095ZMaRDI QIDQ5082441

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Publication date: 16 June 2022

Published in: Filomat (Search for Journal in Brave)

zbMATH Keywords

matrix inversionsimulation verificationnonlinear activation functiongradient-based neural network

Mathematics Subject Classification ID

Theory of matrix inversion and generalized inverses (15A09)

Cites Work

• A new design formula exploited for accelerating Zhang neural network and its application to time-varying matrix inversion
• Finite iterative algorithms for extended Sylvester-conjugate matrix equations
• Global exponential convergence and stability of gradient-based neural network for online matrix inversion
• A class of finite-time dual neural networks for solving quadratic programming problems and its \(k\)-winners-take-all application
• A finite-time recurrent neural network for solving online time-varying Sylvester matrix equation based on a new evolution formula
• 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
• A recurrent neural network for real-time matrix inversion
• Noise-Tolerant ZNN Models for Solving Time-Varying Zero-Finding Problems: A Control-Theoretic Approach
• Regularized image reconstruction using SVD and a neural network method for matrix inversion
• Two New Types of Zhang Neural Networks Solving Systems of Time-Varying Nonlinear Inequalities
• From Zhang Neural Network to Newton Iteration for Matrix Inversion

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