Neural networks for computing eigenvalues and eigenvectors

DOI10.1007/BF00201437zbMath0769.65015WikidataQ60487040 ScholiaQ60487040MaRDI QIDQ1202354

Andrzej Cichocki, Rolf Unbehauen

Publication date: 23 February 1993

Published in: Biological Cybernetics (Search for Journal in Brave)

zbMATH Keywords

unconstrained optimizationneural networkseigenvalueseigenvectorssteepest descent algorithmtest problemseigendecomposition

Mathematics Subject Classification ID

Numerical computation of eigenvalues and eigenvectors of matrices (65F15)

Related Items

A recurrent neural network for computing pseudoinverse matrices, Continuous methods for extreme and interior eigenvalue problems, A concise functional neural network computing the largest modulus eigenvalues and their corresponding eigenvectors of a real skew matrix, A fast continuous method for the extreme eigenvalue problem, Real-time computation of the eigenvectors of a class of positive definite matrices, Tensor logarithmic norm and its applications, Robust \(H_\infty\) synchronization of Markov jump stochastic uncertain neural networks with decentralized event-triggered mechanism, A deep learning method for computing eigenvalues of the fractional Schrödinger operator, Application of machine learning regression models to inverse eigenvalue problems, Zhang Neural Networks for fast and accurate computations of the field of values, Continuous methods for symmetric generalized eigenvalue problems, A recurrent neural network computing the largest imaginary or real part of eigenvalues of real matrices, Neural networks for computing eigenvalues and eigenvectors, A functional neural network computing some eigenvalues and eigenvectors of a special real matrix, Analog approach for the eigen-decomposition of positive definite matrices, Signal flow graphs and neural networks

Cites Work

• Unnamed Item
• Systolic designs for eigenvalue-eigenvector computations using matrix powers
• On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix
• A neural network for computing eigenvectors and eigenvalues
• Neural networks for computing eigenvalues and eigenvectors
• A survey of conjugate gradient algorithms for solution of extreme eigen-problems of a symmetric matrix
• Unconstrained Variational Principles for Eigenvalues of Real Symmetric Matrices
• Neural networks for solving systems of linear equations and related problems
• Neurons with graded response have collective computational properties like those of two-state neurons.