Stabilization to Exponential Input-to-State Stability of a Class of Neural Networks with Delay by Observer-Based Aperiodic Intermittent Control
This study is devoted to investigating the stabilization to exponential input-to-state stability (ISS) of a class of neural networks with time delay and external disturbances under the observer-based aperiodic intermittent control (APIC). Compared with the general neural networks, the state of the n...
Saved in:
Main Authors: | Mengyue Li, Biwen Li, Yuan Wan |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2021-01-01
|
Series: | Discrete Dynamics in Nature and Society |
Online Access: | http://dx.doi.org/10.1155/2021/9923792 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Observer-Based Aperiodic Time-Triggered Intermittent Control for Exponential Consensus of Multi-Agent Systems
by: Xueming Xu, et al.
Published: (2025-01-01) -
Stochastic Stabilization of Malware Propagation in Wireless Sensor Network via Aperiodically Intermittent White Noise
by: Xiaojing Zhong, et al.
Published: (2020-01-01) -
Input-to-State Stability for Dynamical Neural Networks with Time-Varying Delays
by: Weisong Zhou, et al.
Published: (2012-01-01) -
Existence and Exponential Stability of Periodic Solution for a Class of Generalized Neural Networks with Arbitrary Delays
by: Yimin Zhang, et al.
Published: (2009-01-01) -
Periodic Solutions and Exponential Stability of a Class of Neural Networks with Time-Varying Delays
by: Yingxin Guo, et al.
Published: (2009-01-01)