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Recurrent neural network model for computing largest and smallest generalized eigenvalue
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DOI：10.1016/j.neucom.2008.05.005.png)
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Abstract
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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.
Keywords:
Recurrent neural network
Generalized eigenvalue
Real symmetric matrix
Convergence
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