Global output convergence of recurrent neural networks with distributed delays

DOI10.1016/j.nonrwa.2005.06.009zbMath1114.34056OpenAlexW2080615176MaRDI QIDQ864215

Jinling Liang, Cao, Jinde

Publication date: 13 February 2007

Published in: Nonlinear Analysis. Real World Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.nonrwa.2005.06.009

zbMATH Keywords

recurrent neural networksdistributed delaysM-matrixglobal output convergencetime-varying input

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Stability theory of functional-differential equations (34K20)

Related Items (13)

State estimation for jumping recurrent neural networks with discrete and distributed delays ⋮ Comparison of convergence and stability properties for the state and output solutions of neural networks ⋮ Global asymptotic stability of uncertain stochastic bi-directional associative memory networks with discrete and distributed delays ⋮ Global output convergence of Cohen-Grossberg neural networks with both time-varying and distributed delays ⋮ Event-triggered state estimation for a class of delayed recurrent neural networks with sampled-data information ⋮ Exponential state estimation for delayed recurrent neural networks with sampled-data ⋮ Convergence of asymptotic systems of non-autonomous neural network models with infinite distributed delays ⋮ Exponential stability for stochastic BAM networks with discrete and distributed delays ⋮ Kalman filtering for discrete stochastic systems with multiplicative noises and random two-step sensor delays ⋮ Dynamic analysis of stochastic fuzzy Cohen-Grossberg neural networks with time-varying delays ⋮ Existence and global exponential stability of periodic solution for impulsive Cohen-Grossberg-type BAM neural networks with continuously distributed delays ⋮ pth moment exponential stability of stochastic fuzzy Cohen–Grossberg neural networks with discrete and distributed delays ⋮ Global dissipativity analysis on uncertain neural networks with mixed time-varying delays

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