Recurrent Neural Networks for Computing Pseudoinverses of Rank-Deficient Matrices

DOI10.1137/S1064827594267161zbMath0891.93034OpenAlexW2034514780MaRDI QIDQ4376230

Jun Wang

Publication date: 10 February 1998

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/s1064827594267161

zbMATH Keywords

neural networkgeneralized inversescomputation of pseudoinverses

Mathematics Subject Classification ID

Related Items (25)

A noise-tolerant fast convergence ZNN for dynamic matrix inversion ⋮ Neural network approach to computing outer inverses based on the full rank representation ⋮ Zhang neural network solving for time-varying full-rank matrix Moore-Penrose inverse ⋮ A Novel Iterative Method for Computing Generalized Inverse ⋮ Recurrent Neural Network Approach Based on the Integral Representation of the Drazin Inverse ⋮ Recurrent Neural Network for Computing Outer Inverse ⋮ Complex Neural Network Models for Time-Varying Drazin Inverse ⋮ Conditions for existence, representations, and computation of matrix generalized inverses ⋮ IMPROVED SUFFICIENT CONDITIONS FOR GLOBAL EXPONENTIAL STABILITY OF RECURRENT NEURAL NETWORKS WITH DISTRIBUTED DELAYS ⋮ Recurrent neural network for computing the \(W\)-weighted Drazin inverse ⋮ Neural network for computing pseudoinverses and outer inverses of complex-valued matrices ⋮ A recurrent neural network computing the largest imaginary or real part of eigenvalues of real matrices ⋮ Properties and computation of continuous-time solutions to linear systems ⋮ Exact solutions and convergence of gradient based dynamical systems for computing outer inverses ⋮ Discrete-time noise-tolerant Zhang neural network for dynamic matrix pseudoinversion ⋮ Two finite-time convergent Zhang neural network models for time-varying complex matrix Drazin inverse ⋮ Recurrent neural networks for computing weighted Moore-Penrose inverse ⋮ Recurrent neural networks for nonlinear output regulation ⋮ From Penrose equations to Zhang neural network, Getz-Marsden dynamic system, and DDD (direct derivative dynamics) using substitution technique ⋮ Exponential stability of hybrid stochastic recurrent neural networks with time-varying delays ⋮ Complex recurrent neural network for computing the inverse and pseudo-inverse of the complex matrix ⋮ Varying-parameter Zhang neural network for approximating some expressions involving outer inverses ⋮ Augmented gradient flows for on-line robust pole assignment via state and output feedback ⋮ General 9-instant discrete-time Zhang neural network for time-dependent applications ⋮ Complex ZNN for computing time-varying weighted pseudo-inverses

This page was built for publication: Recurrent Neural Networks for Computing Pseudoinverses of Rank-Deficient Matrices