基于大脑状态指数的粒子群优化-模糊麻醉闭环控制
发布时间：2018-08-17 11:23
【摘要】：针对麻醉用药的个体差异,术中麻醉维持的复杂性和不确定性,以及当前监测手段存在的缺陷,传统麻醉深度PID控制器不能满足其非线性控制需要,而以往麻醉深度(DOA)模糊控制器的规则完全依赖于经验调节,因此无法达到预期的控制效果。本研究建立了以大脑状态指数(CSI)为反馈变量的模糊麻醉闭环控制系统,并采用粒子群算法同时优化基于CSI的变化和异丙酚输出率之间的模糊控制规则和隶属度函数。通过系统仿真将CSI值的目标设定在40和30,并加入高斯噪声以模拟临床干扰。实验表明,该系统能准确、快速、平稳地达到CSI预设值,且在噪声干扰下,无明显扰动。经粒子群优化(PSO)过的基于CSI模糊控制器应用在DOA闭环控制系统具有较好的稳定性及鲁棒性。
[Abstract]:In view of the individual differences in anesthetic use, the complexity and uncertainty of anesthetic maintenance during operation, and the defects of current monitoring methods, the traditional PID controller for anesthetic depth can not meet its nonlinear control needs. In the past, the rule of (DOA) fuzzy controller for anesthetic depth was completely dependent on experience adjustment, so it could not achieve the desired control effect. In this study, a fuzzy closed loop control system based on cerebral state index (CSI) was established. Particle swarm optimization (PSO) was used to optimize the fuzzy control rules and membership function based on CSI and propofol output rate simultaneously. The target of CSI is set at 40 and 30 by system simulation, and Gao Si noise is added to simulate clinical interference. The experimental results show that the system can reach the CSI preset accurately, quickly and smoothly, and there is no obvious disturbance under the noise disturbance. The application of CSI fuzzy controller based on CSI fuzzy controller based on particle swarm optimization (PSO) (PSO) overpass has good stability and robustness in DOA closed-loop control system.
【作者单位】： 中南大学地球科学与信息物理学院;中南大学湘雅医院麻醉科;
【基金】：国家自然科学基金资助项目(81171053)
【分类号】：R312;R614
本文编号：2187472
[Abstract]:In view of the individual differences in anesthetic use, the complexity and uncertainty of anesthetic maintenance during operation, and the defects of current monitoring methods, the traditional PID controller for anesthetic depth can not meet its nonlinear control needs. In the past, the rule of (DOA) fuzzy controller for anesthetic depth was completely dependent on experience adjustment, so it could not achieve the desired control effect. In this study, a fuzzy closed loop control system based on cerebral state index (CSI) was established. Particle swarm optimization (PSO) was used to optimize the fuzzy control rules and membership function based on CSI and propofol output rate simultaneously. The target of CSI is set at 40 and 30 by system simulation, and Gao Si noise is added to simulate clinical interference. The experimental results show that the system can reach the CSI preset accurately, quickly and smoothly, and there is no obvious disturbance under the noise disturbance. The application of CSI fuzzy controller based on CSI fuzzy controller based on particle swarm optimization (PSO) (PSO) overpass has good stability and robustness in DOA closed-loop control system.
【作者单位】： 中南大学地球科学与信息物理学院;中南大学湘雅医院麻醉科;
【基金】：国家自然科学基金资助项目(81171053)
【分类号】：R312;R614
【相似文献】
相关期刊论文 前2条
1 王冬梅;张芬;任石玄;周桂容;李虹;周萍;;基于模糊综合评判的病人满意度评价模型[J];数理医药学杂志;2006年02期
2 黄炳荣;“模糊控制”模型在疾病流行预测中的应用[J];数理医药学杂志;1994年02期
，本文编号：2187472
本文链接：https://www.wllwen.com/yixuelunwen/swyx/2187472.html
