Treffer: Fast Adaptive Extraction Algorithm for Multiple Principal Generalized Eigenvectors
Title:
Fast Adaptive Extraction Algorithm for Multiple Principal Generalized Eigenvectors
Authors:
Source:
Recent Advances in Intelligent TechniquesInternational journal of intelligent systems. 28(3):289-306
Publisher Information:
Hoboken, NJ: Wiley, 2013.
Publication Year:
2013
Physical Description:
print, 27 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Algèbre, Algebra, Algèbre linéaire et multilinéaire, matrices, Linear and multilinear algebra, matrix theory, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Approximation numérique, Numerical approximation, Algorithme adaptatif, Adaptive algorithm, Algoritmo adaptativo, Algorithme parallèle, Parallel algorithm, Algoritmo paralelo, Algorithme rapide, Fast algorithm, Algoritmo rápido, Approximation stochastique, Stochastic approximation, Aproximación estocástica, Convergence asymptotique, Asymptotic convergence, Convergencia asintótica, Efficacité, Efficiency, Eficacia, Méthode Newton, Newton method, Método Newton, Méthode séquentielle, Sequential method, Método secuencial, Optimisation sans contrainte, Unconstrained optimization, Optimización sin restricción, Problème valeur propre, Eigenvalue problem, Problema valor propio, Réseau neuronal, Neural network, Red neuronal, Système dégénéré, Degenerate system, Sistema degenerado, Temps réel, Real time, Tiempo real, Théorie stochastique, Stochastic theory, Teoría estocástica, Traitement signal, Signal processing, Procesamiento señal
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Automation, University of Science and Technology of China, Hefei, Anhui 230027, China
ISSN:
0884-8173
Rights:
Copyright 2014 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.27283771
Database:
PASCAL Archive
Weitere Informationen
We consider adaptively extracting multiple principal generalized eigenvectors, which can be widely applied in modern signal processing. By using the deflation technique, the problem is reformulated into an unconstrained minimization problem. An adaptive sequential algorithm based on the Newton method is proposed to solve this problem. To improve its real-time performance, a parallel version of this algorithm is provided on the basis of certain approximation. Furthermore, a two-layer neural network is constructed to execute the adaptive algorithm. The asymptotic convergence of this algorithm is rigorously proved by stochastic approximation theory. The simulation results demonstrate the effectiveness of the proposed algorithms.