Stabilization analysis of impulsive state-dependent neural networks with nonlinear disturbance: a quantization approach
type: Item
instance of: scholarly article
title: Stabilization analysis of impulsive state-dependent neural networks with nonlinear disturbance: a quantization approach
zbMATH Open document ID: 1465.93160
author: Yaxian Hong
author: Honghua Bin
author: Zhenkun Huang
published in: International Journal of Applied Mathematics and Computer Science
publication date: 2021-05-03T00:00:00Z
Mathematics Subject Classification ID: 93D15
Mathematics Subject Classification ID: 93D25
Mathematics Subject Classification ID: 93D20
Mathematics Subject Classification ID: 93B70
zbMATH DE Number: 7342350
zbMATH Keywords: state-dependent neural networks
zbMATH Keywords: quantized input
zbMATH Keywords: stabilization
MaRDI profile type: Publication
full work available at URL: 06 hong bin huang stabilization analysis of impulsive 2020 2.pdf
OpenAlex ID: W3044966697
cites work: Q3324260
cites work: Synchronization of state-switching Hopfield-type neural networks: a quantized level set approach
cites work: Q3995625
cites work: Smooth stabilization implies coprime factorization
cites work: Novel stability criteria for impulsive memristive neural networks with time-varying delays
cites work: Event-Triggered Control for Consensus Problem in Multi-Agent Systems With Quantized Relative State Measurements and External Disturbance
cites work: Synchronization of delayed complex dynamical networks with impulsive and stochastic effects
cites work: Novel conditions on exponential stability of a class of delayed neural networks with state-dependent switching
DOI: 10.34768/AMCS-2020-0021
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description: scientific article; zbMATH DE number 7342350