摘要
【目的】为探索接触辉光放电过程中的伏安特性和等离子体中·OH的含量变化。【方法】构建一种Cross-BP(Backpropagationnetwork)神经网络模型和遗传算法(GeneticAlgorithm)优化的GA-BP神经网络模型,预测在不同电压、放电时间、电解质初始浓度等条件下的伏安特性和·OH浓度、pH和电导率。【结果】建立的Cross-BP模型(结构2-11-1)和GA-BP(结构3-8-3)模型经精度(调整后的R2≥0.9921)和试验验证能在一定误差内预测放电过程中伏安特性和·OH的变化,且GA-BP模型预测结果的MSE、MAE值更低。【结论】所构建的GA-BP模型对等离子体中·OH含量变化的预测具有高精确度和高效率。
【Objective】To study the magnitude of the volt-ampere characteristic and the content of hy-droxyl radicals in the plasma during the contact discharge process.【Method】Constructed a Cross-BP(Back Propagation Network)neural network model and genetic algorithm(Genetic Algorithm)optimized GA-BP neural network model,predicted the volt-ampere characteristics and hydroxyl radical concentration,pH,and conductivity of the electrolyte at different voltages,discharge times,electrolyte initial concentrations using two models respectively.【Result】Both the established Cross-BP model(structure 2-11-1)and GA-BP model(structure 3-8-3)can predict the changes of volt-ampere characteristic and hydroxyl radical dur-ing the discharge process in a certain error through accuracy(adjusted≥0.9921)and experimental verifica-tion,the MSE and MAE value of the GA-BP model is lower.【Conclusion】The obtained GA-BP model has high accuracy and efficiency in predicting the change of hydroxyl radical content in the plasma.
作者
曹文强
万强贵
龙海涛
徐毓鸿
王婷
薛华丽
蒲陆梅
CAO Wenqiang;WAN Qianggui;LONG Haitao;XU Yuhong;WANG Ting;XUE Huali;PU Lumei(College of Science,Gansu Agricultural University,Lanzhou 730070,China)
出处
《甘肃农业大学学报》
CAS
CSCD
2023年第6期236-248,共13页
Journal of Gansu Agricultural University
基金
国家自然科学基金项目(31860472)。
