New Global Asymptotic Robust Stability of Dynamical Delayed Neural Networks via Intervalized Interconnection Matrices

This article identifies a new upper bound norm for the intervalized interconnection matrices pertaining to delayed dynamical neural networks under the parameter uncertainties. By formulating the appropriate Lyapunov functional and slope-bounded activation functions, the derived new upper bound norms...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2022-11, Vol.52 (11), p.11794-11804
Main Authors: Gunasekaran, Nallappan, Thoiyab, N. Mohamed, Zhu, Quanxin, Cao, Jinde, Muruganantham, P.
Format: Article
Language:English
Subjects:
Artificial neural networks
Asymptotic properties
Asymptotic stability
Delay effects
Delays
Dynamic stability
Dynamical delayed neural networks
Functionals
global asymptotic robust stability
Lyapunov functional
Neural networks
Neurons
Norms
Numerical methods
parameter uncertainties
Parameter uncertainty
Robust stability
Robustness (mathematics)
slope-bounded activation functions
Upper bound
Upper bounds
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Summary:This article identifies a new upper bound norm for the intervalized interconnection matrices pertaining to delayed dynamical neural networks under the parameter uncertainties. By formulating the appropriate Lyapunov functional and slope-bounded activation functions, the derived new upper bound norms provide new sufficient conditions corresponding to the equilibrium point of the globally asymptotic robust stability with respect to the delayed neural networks. The new upper bound norm also yields the optimized minimum results as compared with some existing methods. Numerical examples are given to demonstrate the effectiveness of the proposed results obtained through the new upper bound norm method.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2021.3079423