New robust stability results for bidirectional associative memory neural networks with multiple time delays

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Küçük Resim

Tarih

2012-08-01

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Yayıncı

Elsevier Science Inc

Erişim Hakkı

info:eu-repo/semantics/closedAccess

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Dergi sayısı

Özet

In this paper, the robust stability problem is investigated for a class of bidirectional associative memory (BAM) neural networks with multiple time delays. By employing suitable Lyapunov functionals and using the upper bound norm for the interconnection matrices of the neural network system, some novel sufficient conditions ensuring the existence, uniqueness and global robust stability of the equilibrium point are derived. The obtained results impose constraint conditions on the system parameters of neural network independent of the delay parameters. Some numerical examples and simulation results are given to demonstrate the applicability and effectiveness of our results, and to compare the results with previous robust stability results derived in the literature.

Açıklama

Anahtar Kelimeler

Equilibrium and stability analysis, Bidirectional associative memory neural networks, Lyapunov functionals, Global asymptotic stability, Reaction-Diffusion terms, Varying delays, Exponential stability, Distributed delays, Neutral-Type, Criterion, Discrete, Passivity

Kaynak

Applied Mathematics and Computation

WoS Q Değeri

Q1
Q1

Scopus Q Değeri

Q1

Cilt

218

Sayı

23

Künye

Senan, S., Arık, S. & Liu, D. (2012). New robust stability results for bidirectional associative memory neural networks with multiple time delays. Applied Mathematics and Computation, 218(23), 11472-11482. doi:10.1016/j.amc.2012.04.075