计量学报2017，Vol.38Issue(2)：209-214,6.DOI:10.3969/j.issn.1000-1158.2017.02.19
基于PSO算法的MFF模型的参数辨识与优化
Parameter Identification and Optimization of the MFF Model Based on the Particle Swarm Optimization Algorithm
滕峰成 1郝宇 1林晓乐1
作者信息
- 1. 燕山大学 电气工程学院, 河北 秦皇岛 066004
- 折叠
摘要
Abstract
Based on the flux theory and Monte-Carlo method, magnetic fluid transmission characteristics are analyzed and the magnetic fluid film (MFF) models of the transmittance and the sensor are established.Using PSO algorithm, the parameter identification of MFF transmission model is carried out.The influences of number of groups, iterations, inertia weight and acceleration factors on operating results of algorithm are analyzed, and the best combination of parameters is selected.The experiment platform of MFF current sensors is built, the impact of MFF transmission of the MFF thickness and particle concentration is analyzed.The results of experiment and simulation show that the predictive error of the model within 2.3% and the measurement sensitivity of the MFF current sensor reach 12 μW/A.关键词
计量学/磁流体薄膜/粒子群算法/电流传感器/透射模型/参数辨识Key words
metrology/MFF/PSO algorithm/current sensor/transmission model/parameter identification分类
通用工业技术引用本文复制引用
滕峰成,郝宇,林晓乐..基于PSO算法的MFF模型的参数辨识与优化[J].计量学报,2017,38(2):209-214,6.
