.png){kind=icon}
返回
Recurrent neural network model for computing largest and smallest generalized eigenvalue
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2008.05.005.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A continuous recurrent neural network model is presented for computing the largest and smallest generalized eigenvalue of a symmetric positive pair (A,B). Convergence properties to the extremum eigenvalues based upon Liapunov functional with the help of the generalized eigen-decomposition theorem is obtained. Compared with other existing models, this model is also suitable for computing the smallest generalized eigenvalue simply by replacing A by -A as well as maintaining invariant norm property. Numerical simulation further shows the effectiveness of the proposed model. (C) 2008 Elsevier B.V. All rights reserved.
Keyword:
Recurrent neural network
Generalized eigenvalue
Real symmetric matrix
Convergence
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
分享收藏A functional neural network for computing the largest modulus eigenvalues and their corresponding eigenvectors of an anti-symmetric matrix.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Unified stabilization approach to principal and minor components extraction algorithms主成分和次要成分提取算法的统一稳定方法NEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
分享收藏A simple functional neural network for computing the largest and smallest eigenvalues and corresponding eigenvectors of a real symmetric matrixNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容