Finite-Time Dissipative Fault Estimate and Event-Triggered Fault-Tolerant Synchronization Control for Discrete Semi-Markov Jumping Neural Networks

This paper studies discrete-time semi-Markov neural networks (NNs) and proposes a finite-time γ-dissipative fault estimate (FE) and fault-tolerant synchronization control (FTSC) with the event-triggered method. Based on the intermediate variable introduced, an FE scheme is presented to obtain estimate information of faults, states, and intermediate variables. According to the event-triggered condition designed, an FTSC protocol is proposed so that the error system between master system and slave system is stochasticslly synchronized within finite time and meets γ-dissipativity. An example shows the validity of this proposed scheme.

Authors

Institutions

Publication Details

Journal
Entropy
Published
2025-11-22
DOI
https://doi.org/10.3390/e27121186
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Finite-Time Dissipative Fault Estimate and Event-Triggered Fault-Tolerant Synchronization Control for Discrete Semi-Markov Jumping Neural Networks

Xiaodan Zhu, Yu Chen, Yanjun Wang
Entropy
Neural Networks Stability and Synchronization
article

Finite-Time Dissipative Fault Estimate and Event-Triggered Fault-Tolerant Synchronization Control for Discrete Semi-Markov Jumping Neural Networks

Xiaodan Zhu, Yu Chen, Yanjun Wang
article en

Abstract

This paper studies discrete-time semi-Markov neural networks (NNs) and proposes a finite-time γ-dissipative fault estimate (FE) and fault-tolerant synchronization control (FTSC) with the event-triggered method. Based on the intermediate variable introduced, an FE scheme is presented to obtain estimate information of faults, states, and intermediate variables. According to the event-triggered condition designed, an FTSC protocol is proposed so that the error system between master system and slave system is stochasticslly synchronized within finite time and meets γ-dissipativity. An example shows the validity of this proposed scheme.

EntropyVol. 27(12)
Ludong University (CN)
Openalex Percentile: Top 59%
Neural Networks Stability and Synchronization
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.

Finite-Time Dissipative Fault Estimate and Event-Triggered Fault-Tolerant Synchronization Control for Discrete Semi-Markov Jumping Neural Networks — Xiaodan Zhu, Yu Chen, et al. · Entropy (2025) | TGRS Research Map | TGRS