间隔类型 2 适应性神经模糊推理系统基于人工心脏起器设计和稳定性分析.
Asghar Dabiri Aghdam1, Nader Jafarnia Dabanloo1, Fereidoun Nooshiravan Rahatabad1
1Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Journal of long-term effects of medical implants
|November 8, 2023
概括
这项研究引入了一个间隔类型2模糊系统 (IT2FS) 适应性神经模糊推理系统 (ANFIS) 节拍器控制器. 这种新的模糊C-means方法显著改善了节拍器性能指标,如上升时间和超速.
科学领域:
- 生物医学工程 生物医学工程
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 之前的研究使用了1型模糊推理系统 (T1FS) 和比例积分导数 (PID) 控制器来控制心脏起器,通常使用最小平方估计和反向传播来调整.
- 在之前的研究中,对1型模糊逻辑 (IT1FS) 模型的分析侧重于步骤响应,突出了性能优化的局限性.
研究的目的:
- 设计和模拟基于IT2FS的先进间隔类型2模糊系统 (IT2FS) 适应性神经模糊推理系统 (ANFIS) 用于节拍器控制.
- 通过改进关键时间域指标,如上升时间,超速和沉降时间来提高节拍器控制器性能.
主要方法:
- 使用MATLAB开发基于IT2FS的ANFIS心脏起器控制器.
- 通过时间域分析 (单位步骤响应) 验证系统稳定性.
- 应用模糊C-Means (FCM) 集群方法来调整会员功能,优于传统算法.
主要成果:
- 与以前的模型相比,设计的IT2FS-ANFIS控制器在上升时间,超标和结算时间方面取得了显著的改进.
- 对于会员函数调整,FCM方法比以前工作中使用的最小平方估计和反向传播算法更有效.
- 设计的模糊逻辑模型的稳定性在时间域中成功验证.
结论:
- IT2FS-ANFIS控制器为起器设计提供了卓越的方法,提供了增强的患者心率调节.
- 在生物医学控制应用中,FCM方法代表了优化模糊推理系统参数的更有效策略.
- 拟议的系统实现了显著的性能提升,为更复杂的心脏节奏解决方案铺平了道路.
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