Exponential stability of discrete-time delayed Hopfield neural networks with stochastic perturbations and impulses

DOI10.1007/s00025-011-0131-zzbMath1252.93127OpenAlexW2009910641MaRDI QIDQ692507

Chuandong Li, Wenfeng Hu, Shukai Duan, Sichao Wu

Publication date: 6 December 2012

Published in: Results in Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00025-011-0131-z

zbMATH Keywords

impulsestochastic perturbationsLyapunov-Krasovskii functionHalanay inequalitymean square exponential stabilitydiscrete-time Hopfleld neural networks

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Asymptotic stability in control theory (93D20) Stochastic stability in control theory (93E15) Stochastic systems in control theory (general) (93E03)

Related Items (2)

Moment stability of nonlinear discrete stochastic systems with time-delays based on \(\mathcal{H}\)-representation technique ⋮ Multi-dimensional discrete Halanay inequalities and the global stability of the disease free equilibrium of a discrete delayed malaria model

Cites Work

• Stability in asymmetric Hopfield nets with transmission delays
• A note on the global stability of generalized difference equations
• Impulsive delay differential inequality and stability of neural networks
• Exponential stability of continuous-time and discrete-time cellular neural networks with delays
• Stability analysis of delayed neural networks
• An Algorithmic Introduction to Numerical Simulation of Stochastic Differential Equations
• Stability results for impulsive differential systems with applications to population growth models
• Linear Matrix Inequalities in System and Control Theory
• Stability Criteria for Impulsive Systems With Time Delay and Unstable System Matrices
• Stability of Solutions for Stochastic Impulsive Systems via Comparison Approach
• Neural networks and physical systems with emergent collective computational abilities.
• Neurons with graded response have collective computational properties like those of two-state neurons.
• Impulsive control theory

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