Recurrent neural network model for computing largest and smallest generalized eigenvalue

Neurocomputing

Volumn 71, Issue 16-18, 2008, Pages 3589-3594

  Liu, Lijun ^a,b   Shao, Hongmei ^c   Nan, Dong ^d  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

^b DALIAN NATIONALITIES UNIVERSITY   (China)

^c CHINA UNIVERSITY OF PETROLEUM   (China)

^d BEIJING UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Convergence; Generalized eigenvalue; Real symmetric matrix; Recurrent neural network

Indexed keywords

DOMAIN DECOMPOSITION METHODS; EIGENVALUES AND EIGENFUNCTIONS; IMAGE CLASSIFICATION; RECURRENT NEURAL NETWORKS; REINFORCEMENT LEARNING; VEGETATION;

CONVERGENCE; CONVERGENCE PROPERTIES; DECOMPOSITION THEOREMS; EIGENVALUES; GENERALIZED EIGENVALUE; LIAPUNOV FUNCTIONAL; NUMERICAL SIMULATIONS; REAL SYMMETRIC MATRIX; RECURRENT NEURAL NETWORK MODELS;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; CONFERENCE PAPER; CONTROLLED STUDY; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PRIORITY JOURNAL; SIGNAL PROCESSING; VALIDITY;

EID: 56549119541     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2008.05.005     Document Type: Conference Paper

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