Dynamic Parameter Model of Plasma Loading Based on Neural Network Algorithm
Most plasma loading electrical models are based on fixed parameter models,ignoring the im-pact of changes in equivalent loading parameters on the model,which can easily lead to significant er-rors.In order to improve the errors caused by chan-ges in equivalent parameters,the variation of load parameters such as equivalent capacitance and e-quivalent resistance with the amplitude and frequen-cy of applied voltage was first investigated.Based on this,a BP neural network parameter adjustment module was trained,and a dynamic parameter mod-el of plasma load was established,achieving the up-date of load equivalent parameters under external excitation changes.The results showed that the sim-ulation accuracy using the neural network dynamic parameter model was 95.70%,while the simulation accuracy using the fixed parameter model was 82.89%,which improved the simulation accuracy by 15.45%,which was of great significance to sim-plify experimental workload and guide the design of plasma reactors.
国家科技期刊平台
NSTL
万方数据
