Dynamic Moore-Penrose Inversion With Unknown Derivatives: Gradient Neural Network Approach

• Published in: IEEE transaction on neural networks and learning systems Vol. 34; no. 12; pp. 10919 - 10929
• Main Authors: Zhang, Yinyan, Zhang, Jilian, Weng, Jian
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Additives; Computational modeling; Convergence; Dynamic matrix Moore–Penrose inversion; finite-time convergence; gradient-based neural network (GNN); Information processing; Integrated circuit modeling; Mathematical models; Neural networks; noise-tolerant neural network; Numerical methods; Real time; Real-time systems; Recurrent neural networks

Finding dynamic Moore-Penrose inverses (DMPIs) in real-time is a challenging problem due to the time-varying nature of the inverse. Traditional numerical methods for static Moore-Penrose inverse are not efficient for calculating DMPIs and are restricted by serial processing. The current state-of-the...