Stability of Stochastic Delayed Recurrent Neural Networks
This paper addresses the stability of stochastic delayed recurrent neural networks (SDRNNs), identifying challenges in existing scalar methods, which suffer from strong assumptions and limited applicability. Three key innovations are introduced: (1) weakening noise perturbation conditions by extendi...
Saved in:
| Main Authors: | Hongying Xiao, Mingming Xu, Yuanyuan Zhang, Shengquan Weng |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/14/2310 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Generalized Mean Square Exponential Stability for Stochastic Functional Differential Equations
by: Tianyu He, et al.
Published: (2024-10-01) -
About stability of equilibria of one system of stochastic delay differential equations with exponential nonlinearity
by: Leonid Shaikhet
Published: (2025-02-01) -
Almost periodic solutions of neutral-type differential system on time scales and applications to population models
by: Jing Ge, et al.
Published: (2025-02-01) -
FURTHER IMPROVED CRITERION FOR EXPONENTIAL STABILITY OF LINEAR SYSTEMS WITH MULTIPLE TIME DELAYS
by: Phan Thanh Nam
Published: (2012-09-01) -
Modeling Financial Bubbles with Optional Semimartingales in Nonstandard Probability Spaces
by: Mohamed Abdelghani, et al.
Published: (2025-03-01)