Absolute componentwise stability of interval hopfield neural networks

The componentwise stability is a special type of asymptotic stability which ensures the individual monitoring of each state-space variable of a dynamical system. For an interval Hopfield neural network (IHNN), sufficient conditions are provided to analyze the absolute componentwise stability with re...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2005-02, Vol.35 (1), p.136-141
Main Authors: Pastravanu, O., Matcovschi, M.-H.
Format: Article
Language:English
Subjects:
Absolute componentwise (exponential) asymptotic stability (CWAS
Algorithms
Asymptotic stability
Computer Simulation
CWEAS
Delay
Hopfield neural networks
Industrial control
Informatics
interval Hopfield neural networks (IHNN)
Models, Theoretical
Neural Networks (Computer)
Robust stability
robustness
Stability analysis
Sufficient conditions
Systems Theory
Testing
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Summary:The componentwise stability is a special type of asymptotic stability which ensures the individual monitoring of each state-space variable of a dynamical system. For an interval Hopfield neural network (IHNN), sufficient conditions are provided to analyze the absolute componentwise stability with respect to a class of activation functions (CAF). Both continuous- and discrete-time dynamics are considered. The conditions are formulated in terms of Hurwitz/Schur stability of a test matrix built from the information about the CAF and the interval matrices defining the IHNN. Some interesting results are derived as particular cases, which allow comparisons with several other works.
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/TSMCB.2004.839246