随机延迟神经网络与扩散项的状态估计:一个两步估计方法
Yu Gao1, Zhi-Yun Zhang2, Xiao-Zhen Liu3
1Department of Mathematics, Harbin Institute of Technology, Weihai, Shandong, 264209, China.
概括
这项研究引入了一种新的两步间隔估计方法,用于随机延迟反应扩散神经网络. 该方法为预期的解决方案值提供间隔估计,增强传统的点估计技术.
科学领域:
- 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
- 控制理论 控制理论
背景情况:
- 随机延迟反应-扩散神经网络是复杂的系统,需要准确的状态估计.
- 传统的点估计方法可能无法完全捕捉这些系统中的不确定性.
- 间隔估计通过提供边界,为系统行为提供了更全面的理解.
研究的目的:
- 为随机延迟反应扩散神经网络提出一种新的两步间隔估计方案.
- 通过提供解决方案预期值的间隔估计,超越传统的点估计.
- 根据观察和误差界限,开发适应性值来估计系统状态.
主要方法:
- 一个强大的观察者被用于最初的点向预期值计算.
- 引入了一个辅助函数来推导观察误差的边界.
- 适应性值是使用观测和导出的误差界限来合成的;观察者增益设计影响了点估计准确性和间隔宽度.
- 峰值对峰值分析用于管理高维系统中的计算复杂性.
主要成果:
- 提出的两步方法成功地为溶液的预期值提供了间隔估计.
- 观察者增益设计有效平衡点估计准确度和错误间隔的宽度.
- 数字模拟证实了开发的间隔估计方案的有效性.
结论:
- 新的两步间隔估计方法为分析随机延迟反应扩散神经网络提供了强大的方法.
- 该技术通过提供间隔边界来增强系统状态估计,这对于理解系统不确定性至关重要.
- 该方法通过成功的数值验证证明了其实际适用性,为该领域的研究人员提供了有价值的工具.
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