现代电子技术2026，Vol.49Issue(10)：7-15,9.DOI:10.16652/j.issn.1004-373x.2026.10.002
基于液态神经网络的超声波飞行时间动态校准算法
Ultrasonic time-of-flight dynamic calibration algorithm based on liquid neural network
摘要
Abstract
An ultrasonic flowmeter based on liquid neural network(LNN)is designed innovatively to solve the problem of insufficient accuracy and poor robustness of traditional flow measurement methods in complex fluid dynamics environment.With its dynamic time series modeling ability and adaptive learning of nonlinear features,LNN can effectively analyze the propagation time difference of ultrasonic signals in the fluid,and overcome the influence of noise interference,fluid turbulence and temperature changes.By constructing lightweight LNN model and combining with multi-path ultrasonic signal feature extraction and time series prediction,the system can realize high precision calculation for the flow velocity and flow rate.The experimental results show that,in comparison with the traditional static neural network model,the measurement error of the proposed method is reduced to less than±0.5%in the dynamic fluid scene,and the response delay is less than 50 ms,which significantly enhances the environmental adaptability and real-time performance of the system.This research can provide a more reliable intelligent sensing solution for industrial process control,energy metering and other fields,and validate the potential application value of LNN in physical signal processing.关键词
液态神经网络/超声波流量计/流量测量/动态时序建模/信号特征提取/时间序列预测Key words
liquid neural network/ultrasonic flowmeter/flow measurement/dynamic time series modeling/signal feature extraction/time series prediction分类
信息技术与安全科学引用本文复制引用
汪蒋杰,周君洋,齐浩,金少杰,朱兵磊,张凯..基于液态神经网络的超声波飞行时间动态校准算法[J].现代电子技术,2026,49(10):7-15,9.基金项目
国家自然科学基金资助项目(11472260) （11472260）
