NDLI: A Concise Functional Neural Network Computing the Largest (Smallest) Eigenvalue and one Corresponding Eigenvector of a Real Symmetric Matrix

Content Provider	IEEE Xplore Digital Library
Author	Yiguang Liu Zhisheng You Liping Cao
Copyright Year	2005
Description	Author affiliation: Inst. of Image & Graphics, Sichuan Univ., Chengdu (Yiguang Liu; Zhisheng You)
Abstract	Quick extraction of 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 its eigenvector. When the FNN is converted into a differential equation, the component analytic solution of this equation is obtained. Using the component solution, the convergence properties are fully analyzed. On the basis of this FNN, the method that can compute the largest (or smallest) eigenvalue and one its eigenvector whether the matrix is non-definite, positive definite or negative definite is designed. Finally, three examples show the validity of the method. Comparing with other neural networks designed for the same aim, the proposed FNN is very simple and concise, so it is very easy to be realized
Starting Page	1334
Ending Page	1339
File Size	647568
Page Count	6
File Format	PDF
ISBN	0780394224
DOI	10.1109/ICNNB.2005.1614878
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Concurrent computing Graphics Symmetric matrices Image analysis Neural networks Differential equations Computer networks Eigenvalues and eigenfunctions Matrix converters Signal analysis
Content Type	Text
Resource Type	Article

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