可达到的集合估计和随机采样数据指数同步马科维斯跳跃神经网络的随时间变化的延迟
Linqi Wang1, Jianwei Xia1, Ju H Park2
1School of Mathematics Science, Liaocheng University, Liaocheng, Shandong, 252000, PR China.
这项研究针对具有时间变化的延迟的马科维亚跳跃神经网络 (MJNNs) 的指数级同步和可达集估计. 与现有方法相比,拟议的方法允许采样数据的时间更长.
科学领域:
- 控制理论 控制理论
- 神经网络的神经网络的神经网络
- 随机系统 随机系统 随机系统
背景情况:
- 研究了随机采样数据的指数同步,用于具有时间变化的延迟的马科维斯跳跃神经网络 (MJNNs).
- 解决了在外部干扰下对MJNN的可达到集估计 (RSE) 问题.
研究的目的:
- 在MJNN的错误系统中开发一种新的方法来实现平均平方指数稳定性.
- 设计一种模式依赖的随机抽样数据控制器,以提高系统性能.
- 为了确保系统状态被限制在定义的圆体内,以达到可达的集合估计.
主要方法:
- 构建一个基于模式的双面循环的赖普诺夫函数 (TSLBLF),假设采样数据周期的伯努利分布.
- 引入随机变量来建模未知输入延迟和采样数据周期.
- 分析单位能量局限的干扰,以证明状态被限制在圆体内.
主要成果:
- 误差系统的平均平方指数稳定性条件的推导.
- 设计一种模式依赖的随机采样数据控制器,用于同步和RSE.
- 通过数值示例和电路模拟,证明拟议的方法产生了比现有方法更长的采样数据周期.
结论:
- 开发的随机采样数据控制策略有效地实现了MJNNs的指数级同步和可达到的集合估计.
- 该方法通过允许更长的允许采样数据周期来提高性能.
- 通过数值模拟和物理电路的验证证实了该方法的实际适用性.
更多相关视频
10:45Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
Published on: May 29, 2017
07:59Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
Published on: June 9, 2023
相关概念视频
Linear time-invariant Systems
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
Sampling Continuous Time Signal
In the...
Propagation of Uncertainty from Random Error
BIBO stability of continuous and discrete -time systems
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
Propagation of Uncertainty from Systematic Error
Linear Approximation in Time Domain
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
