人工神经网络
激发
反向传播
自适应神经模糊推理系统
遗传算法
磁场
电磁线圈
算法
计算机科学
控制理论(社会学)
工程类
模糊逻辑
物理
模糊控制系统
人工智能
机器学习
电气工程
控制(管理)
量子力学
作者
Li Xu,Dan Zhao,Yonghua Song,Xiaojie Wang,Jidong Liu,Longyun Kang,Lide Fang
出处
期刊:IEEE Sensors Journal
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:24 (1): 1006-1017
标识
DOI:10.1109/jsen.2023.3331537
摘要
This article proposes a back propagation (BP) neural network based on genetic algorithm (GA) optimization and uses the grey wolf optimizer (GWO) to search for the optimal excitation structure of an electromagnetic flowmeter (EFT). The excitation structure of the EFT is simulated by numerical simulations, and the magnetic field distribution is compared with that of the original machine to demonstrate the correctness of the simulations. Then, such numerical simulations are carried out on many excitation structural models, which are constructed using the pole shoe. The bending angle of the excitation coil and the height from the pipe wall are applied as input sets, while the magnetic field strength and uniformity are applied as output sets and then compared with a single hidden layer BP neural network and a double hidden layer BP neural network, radial basis function (RBF) neural network, and adaptive neuro-fuzzy inference system (ANFIS). The results indicate that using GAs for optimizing neural networks yields the best performance. The simulation and modeling of the optimal excitation structure results in the larger-the-better characteristic of magnetic field strength, it improved by 72%. The smaller-the-better characteristic of magnetic field uniformity decreased by 3.7%. The overall satisfaction level increased by 78%.
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