基于GA-PID和GA-RBF-PID算法设计的闭环控制方案,用于大脑动态调制
Chengxia Sun1, Lijun Geng1, Xian Liu2
1Mechanical and Electrical Engineering College, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China.
Entropy (Basel, Switzerland)
|November 24, 2023
概括
本研究介绍了使用遗传算法 (GA) 和辐射基函数 (RBF) 神经网络的先进的神经刺激控制方案,以使神经精神疾病中的大脑动态正常化,提高治疗的准确性和稳定性.
科学领域:
- 生物医学工程 生物医学工程
- 计算神经科学是一种神经科学.
- 控制系统 控制系统
背景情况:
- 神经精神疾病的特点是神经振荡异常.
- 神经刺激提供了一种调节大脑动态和恢复正常神经活动的方法.
- 现有的神经刺激控制方案通常依赖于经验参数调整.
研究的目的:
- 提出和评估用于神经刺激的新型闭环控制方案.
- 通过温德林型合神经质量模型模拟的调节大脑动态.
- 为了提高神经刺激控制的准确性,动态性能和稳定性.
主要方法:
- 开发了两个改进的增量比例积分导数 (PID) 算法.
- 整合一个遗传算法 (GA) 来优化PID控制参数.
- 整合一个辐射基函数 (RBF) 神经网络,用于自适应控制参数调整.
- 使用温德林型合神经质量模型对大脑动态进行模拟.
主要成果:
- 证明GA-PID和GA-RBF-PID闭环控制方案在调节大脑动态方面的高精度.
- 在动态性能和稳定性方面证实了GA-RBF-PID算法的优势.
- 验证了基于模型的假设和预测对神经刺激控制的有效性.
结论:
- 拟议的GA-PID和GA-RBF-PID控制方案有效调节潜在神经精神疾病治疗的脑动态.
- 与GA-PID相比,GA-RBF-PID方案提供了更好的动态性能和稳定性.
- 这项研究有助于推进生物医学工程中早期干预和康复的神经刺激设备的发展.
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