模糊RBF神经网络在麻醉深度控制系统中的应用

Application of Fuzzy RBF Neural Network Control System in Depth of Anesthesia

麻醉深度通常用病人的平均动脉压(MAP)值来直接反应和度量。针对平均动脉压的时变、非线性特点,提出了基于模糊RBF神经网络的麻醉深度PID控制系统。通过采用模糊RBF神经网络对检测到的平均动脉压值进行模糊化处理及神经网络辨识,从而在线整定PID控制器各个参数,以获得更好的控制效果。MATLAB仿真结果表明模糊RBF神经网络用于麻醉深度控制具有良好的动态响应性能。

In the perioperative process,the patient＇s mean arterial pressure（MAP） value was used to reflect the anesthesia depth.As the mean arterial pressure was a time-varying and nonlinear variable,fuzzy RBF neural network control system in depth of anesthesia was studied.The detected MAP value was fuzzily processed and identified by fuzzy RBF neural so that PID controller tuned various parameters on-line by fuzzy RBF neural network.Fuzzy RBF neural network controller performance simulation was realized by software MATLAB,the results showed that the fuzzy RBF neural network control system for anesthesia depth control has better dynamic responses performance.