基于IPSO-BP神经网络算法的微高压氧舱氧气浓度控制研究  

Research on oxygen concentration control of micro hyperbaric oxygen chamber based on IPSO-BP neural network algorithm

作　　者：姬鹏飞 王晓芬 金远远 Ji Pengfei;Wang Xiaofen;Jin Yuanyuan(Anyang Institute of Technology,Anyang,455000,Henan,China)

机构地区：[1]安阳工学院电子信息与电气工程学院,河南安阳455000

出　　处：《船电技术》2025年第1期1-4,共4页Marine Electric & Electronic Engineering

基　　金：安阳市重点研发与推广专项项目(2023C01SF125)。

摘　　要：针对目前微高压氧舱内氧气浓度控制效果不佳、自动化程度不高等问题,本文在分析了微高压氧舱供氧系统工作原理的基础上,对舱内氧气浓度控制过程进行建模,并使用IPSO-BP神经网络PID控制算法对氧气浓度进行优化控制。仿真结果表明,与BP神经网络PID算法、PSO-BP神经网络PID算法相比,IPSO-BP神经网络PID算法有效结合了IPSO全局搜索最优和BP神经网络非线性映射等优点,控制效果快速、准确,且具有较好的抗干扰能力,具有一定的实用价值。In response to the poor control effect and low automation level of oxygen concentration in micro hyperbaric oxygen chambers,this article analyzes the working principle of the oxygen supply system in micro hyperbaric oxygen chambers,models the process of oxygen concentration control in the chamber,and uses IPSO-BP neural network PID control algorithm to optimize the control of oxygen concentration.The simulation results show that compared with BP neural network PID algorithm and PSO-BP neural network PID algorithm,IPSO-BP neural network PID algorithm effectively combines the advantages of IPSO global search optimization and BP neural network nonlinear mapping,and has fast and accurate control effect,good anti-interference ability,which has certain practical value.

关 键 词：IPSO BP神经网络 氧气浓度控制 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]