๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Neural networks based approach for computing eigenvectors and eigenvalues of symmetric matrix

โœ Scribed by Zhang Yi; Yan Fu; Hua Jin Tang


Publisher
Elsevier Science
Year
2004
Tongue
English
Weight
520 KB
Volume
47
Category
Article
ISSN
0898-1221

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


Another neural network based approach fo
โœ Ying Tang; Jianping Li ๐Ÿ“‚ Article ๐Ÿ“… 2010 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 390 KB

This paper introduces a novel neural network based approach for extracting the eigenvalues with the largest or smallest modulus of real skew-symmetric matrices, as well as the corresponding eigenvectors. To this end, unlike the previous neural network based methods that can be summarized by some 2n-

Computation of eigenvalues and eigenvect
โœ David J. Evans ๐Ÿ“‚ Article ๐Ÿ“… 1977 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 556 KB

A recursive algorithm for the implicit derivation of the determinant of a symmetric quindiagonal matrix is developed in terms of its leading principal minors. The algorithm is shown to yield a Sturmian sequence of polynomials from which the eigenvalues can be obtained by use of the bisection process

A functional neural network computing so
โœ Yiguang Liu; Zhisheng You; Liping Cao ๐Ÿ“‚ Article ๐Ÿ“… 2005 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 256 KB

How to quickly compute eigenvalues and eigenvectors of a matrix, especially, a general real matrix, is significant in engineering. Since neural network runs in asynchronous and concurrent manner, and can achieve high rapidity, this paper designs a concise functional neural network (FNN) to extract s