基于学习的滑动模式对分数顺序的Hindmarsh-Rose神经元模型进行同步,具有决定性学习.
Danfeng Chen1, Junsheng Li1, Chengzhi Yuan2
1School of Mechatronic Engineering and Automation, Foshan University, Foshan, China.
Frontiers in neuroscience
|October 13, 2023
概括
这项研究引入了一种基于学习的新型滑动模式控制,用于分数顺序的Hindmarsh-Rose神经元模型. 它通过学习和重用神经动态,在未知的环境中实现快速和适应性的同步.
科学领域:
- 计算神经科学是一种神经科学.
- 控制理论 控制理论
- 人工智能的人工智能
背景情况:
- 神经同步对于神经信号处理和机体功能至关重要.
- 对于神经模型的现有同步方法存在参数依赖性和有限的适应性.
- 分数顺序的Hindmarsh-Rose (FOHR) 神经元模型在动态环境中提出了独特的同步挑战.
研究的目的:
- 为了解决当前神经同步技术的局限性.
- 为FOHR神经模型开发一个强大而适应性的控制策略.
- 在未知的动态环境中研究同步.
主要方法:
- 一个基于学习的滑动模式控制算法,利用确定性学习 (DL) 机制.
- 准确识别和存储未知的FOHR系统动态,使用常量神经网络.
- 基于模型和基于重新学习的控制器的设计,以实现高效的同步任务.
主要成果:
- 通过快速回忆学习的神经元动态来实现快速同步,减少在线计算.
- 通过可重复使用的,存储的控制体验,不断提高同步速度和准确性.
- 证明了适应新同步任务的适应性,而无需重新训练控制参数.
结论:
- 提出的基于DL的滑动模式控制为FOHR神经同步提供了有效的解决方案.
- 该方法克服了对参数的依赖性,并提高了在动态环境中的适应性.
- 这种方法为推进神经控制和同步研究提供了基础.
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