通过基于极端学习机器的扩展状态观察者闭环抓获调制
Zhiyao Zhao1,2, Zijin Wang1,2, Wei Wei3
1School of Artificial Intelligence, Beijing Technology and Business University, Beijing, 100048 China.
Cognitive neurodynamics
|June 2, 2023
概括
这项研究引入了针对的改进的神经调节技术,通过使用基于极端学习机器的观察员来更准确地估计干扰,增强实时控制.
科学领域:
- 神经科学是一个神经科学.
- 控制工程 控制工程 控制工程
- 计算智能是一种计算智能.
背景情况:
- 目前用于的神经调节缺乏基于患者反应的实时适应性控制.
- 现有的线性活性干扰排斥控制方法在准确估计由于稳定状态错误的干扰方面存在局限性.
研究的目的:
- 开发一种更准确,更适应的神经调节策略来治疗.
- 改善对动态的时间变化的总干扰的估计.
主要方法:
- 提出了一个基于极端学习机器 (ELM) 的扩展状态观察员 (ESO) 来增强干扰估计.
- 利用了ELM对非线性时间变量函数的快速准确近似的能力.
- 分析了拟议的基于ELM的ESO的融合和闭环稳定性.
主要成果:
- 基于ELM的ESO在估计总干扰方面明显优于线性ESO.
- 实现了更准确的干扰估计,从而改善了闭环神经调节.
- 通过数值模拟证明了拟议方法的有效性.
结论:
- 基于ELM的ESO为神经调节中的实时干扰估计提供了一种优越的方法.
- 这一进步使得更精确,更有效的自适应性控制成为可能.
- 这些发现支持了先进的计算方法在临床神经治疗中的潜力.
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