A noise-tolerant fast convergence ZNN for dynamic matrix inversion

DOI10.1080/00207160.2021.1881498zbMath1492.65065OpenAlexW3123516373MaRDI QIDQ5033410

Jianqiang Gong, Jie Jin

Publication date: 22 February 2022

Published in: International Journal of Computer Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207160.2021.1881498

zbMATH Keywords

recurrent neural network (RNN)dynamic matrix inversion (DMI)NTFCZNNOZNN

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Direct numerical methods for linear systems and matrix inversion (65F05)

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Cites Work

• A new method for computing Moore-Penrose inverse through Gauss-Jordan elimination
• Zhang neural network solving for time-varying full-rank matrix Moore-Penrose inverse
• Global exponential convergence and stability of gradient-based neural network for online matrix inversion
• An efficient computation of generalized inverse of a matrix
• A new noise-tolerant and predefined-time ZNN model for time-dependent matrix inversion
• Continuous and discrete zeroing neural dynamics handling future unknown-transpose matrix inequality as well as scalar inequality of linear class
• Noise-Tolerant ZNN Models for Solving Time-Varying Zero-Finding Problems: A Control-Theoretic Approach
• Recurrent Neural Networks for Computing Pseudoinverses of Rank-Deficient Matrices
• Nonlinear Feedback Design for Fixed-Time Stabilization of Linear Control Systems

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