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Neural Processing Letters
Published in: Neural Processing Letters 2/2020

08-01-2020

Lagrange Stability for Delayed-Impulses in Discrete-Time Cohen–Grossberg Neural Networks with Delays

Authors: Wenlin Jiang, Liangliang Li, Zhengwen Tu, Yuming Feng

Published in: Neural Processing Letters | Issue 2/2020

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Abstract

In this paper, the problem of exponential Lagrange stability for delayed-impulses in discrete-time Cohen–Grossberg neural networks (CGNNs) with delays is considered. By establishing a novel convergent difference inequation, combining with inductive method and Lyapunov theory, some sufficient conditions are obtained to ensure the exponential Lagrange stability for delayed-impulses in discrete-time CGNNs. Meanwhile, the exponential convergent domain for network is given. Finally, some examples with their simulations are given to verify the effectiveness of our results.
previous article A Novel Model Predictive Runge–Kutta Neural Network Controller for Nonlinear MIMO Systems
next article Global Lagrange Stability of Inertial Neutral Type Neural Networks with Mixed Time-Varying Delays

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Metadata
Title
Lagrange Stability for Delayed-Impulses in Discrete-Time Cohen–Grossberg Neural Networks with Delays
Authors
Wenlin Jiang
Liangliang Li
Zhengwen Tu
Yuming Feng
Publication date
08-01-2020
Publisher
Springer US
Published in
Neural Processing Letters / Issue 2/2020
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-020-10190-2

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