西南交通大学学报2018，Vol.53Issue(2)：367-373,384,8.DOI:10.3969/j.issn.0258-2724.2018.02.020
基于PSO优化RBF-NN的磁浮车间隙传感器温度补偿
Temperature Compensation of Maglev Vehicle Gap Sensor Based on RBF-NN Optimized by PSO
摘要
Abstract
In order to solve the temperature drift problem of a maglev vehicle gap sensor,a temperature compensator based on RBF-NN (radial basis function neural network) was designed to compensate the temperature drift error.A hybrid algorithm was proposed to combine PSO (particle swarm optimization) algorithm with gradient descent algorithm.In the proposed algorithm,the global optimal particle of the PSO was optimized by the gradient descent method.The hybrid algorithm has stronger optimization ability.The compensation model was optimized by the hybrid algorithm and the accuracy of the compensation model was considerably improved.Finally,the compensation model was implemented in FPGA (field-programmable gate array).Experimental results show that temperature drift error of the gap sensor can be compensated effectively.The compensated output of the gap sensor was independent of the temperature.The gap sensor provides correct gap data with a maximum error of 0.45 mm for full scale and a maximum error of 0.16 mm for a working gap from 8 mm to 12 mm.关键词
磁浮列车/间隙传感器/温度补偿/RBF网络/梯度下降法/粒子群优化Key words
maglev vehicles/gap sensor/temperature compensation/RBF network/gradient descent method/particle swarm optimization分类
信息技术与安全科学引用本文复制引用
靖永志,何飞,廖海军,王滢,刘国清,董金文..基于PSO优化RBF-NN的磁浮车间隙传感器温度补偿[J].西南交通大学学报,2018,53(2):367-373,384,8.基金项目
国家自然科学基金资助项目(51377004) （51377004）
中央高校基本科研业务费专项资金资助项目(2682015CX029) （2682015CX029）
