Name
Title | ||
|---|---|---|
A concise functional neural network for computing the extremum eigenpairs of real symmetric matrices |
Abstract | ||
|---|---|---|
Quick extraction of the extremum eigenpairs of a real symmetric matrix is very important in engineering. Using neural networks to complete this operation is in a parallel manner and can achieve high performance. So, this paper proposes a very concise functional neural network (FNN) to compute the largest (or smallest) eigenvalue and one corresponding eigenvector. After transforming the FNN into a differential equation, and obtaining the analytic solution, the convergence properties are completely analyzed. By this FNN, the method that can compute the extremum eigenpairs whether the matrix is non-definite, positive definite or negative definite is designed. Finally, three examples show the validity. Comparing with the other ones used in the same field, the proposed FNN is very simple and concise, so it is very easy to realize. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11759966_61 | ISNN (1) |
Keywords | Field | DocType |
convergence property,extremum eigenpairs,real symmetric matrix,proposed fnn,differential equation,neural network,analytic solution,corresponding eigenvector,high performance,concise functional neural network,eigenvectors,symmetric matrices,positive definite,symmetric matrix | Convergence (routing),Differential equation,Computer science,Matrix (mathematics),Positive-definite matrix,Recurrent neural network,Algorithm,Symmetric matrix,Artificial neural network,Eigenvalues and eigenvectors | Conference |
Volume | ISSN | ISBN |
3971 | 0302-9743 | 3-540-34439-X |
Citations | PageRank | References |
1 | 0.71 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiguang Liu | 1 | 338 | 37.15 |
| Zhisheng You | 2 | 417 | 52.22 |