Mean-Square Exponential Stability of Stochastic Complex-Valued Neural Networks with Impulses

This paper studies the mean-square exponential stability of stochastic complex-valued neural networks (SCVNNs) with impulses. Using a Lyapunov function and the complex Itô formula, sufficient conditions for stability are derived. Numerical examples are provided to verify the results.

Authors

Zhilin Zeng (ORCID: https://orcid.org/0000-0003-3195-4010)
Yuefei Yuan
Xiaobin Wu (ORCID: https://orcid.org/0000-0003-1860-0920)

Publication Details

Published: 2024-12-13
DOI: https://doi.org/10.1109/icnc64304.2024.10987856
Primary Topic: Neural Networks and Applications
Type: article
Field-Weighted Citation Impact: 0.00

Controls

|||

ALL TIME

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

article

Mean-Square Exponential Stability of Stochastic Complex-Valued Neural Networks with Impulses

Zhilin Zeng, Yuefei Yuan, Xiaobin Wu

Dec 13, 2024

Neural Networks and Applications

article

Mean-Square Exponential Stability of Stochastic Complex-Valued Neural Networks with Impulses

Zhilin Zeng, Yuefei Yuan, Xiaobin Wu

December 13, 2024article en

Abstract

Openalex Percentile: Top 73%

Neural Networks and Applications

DOI Source OpenAlex

AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Mean-Square Exponential Stability of Stochastic Complex-Valued Neural Networks with Impulses — Zhilin Zeng, Yuefei Yuan, et al. · (2024) | TGRS Research Map | TGRS