共轭梯度法
校准
人工神经网络
过程(计算)
非线性系统
计算机科学
反向
反问题
人工智能
控制理论(社会学)
算法
数学
物理
统计
几何学
控制(管理)
量子力学
操作系统
数学分析
作者
Karam M.Z. Othman,Abdulkreem Mohammed Salih
出处
期刊:Bulletin of Electrical Engineering and Informatics
[Institute of Advanced Engineering and Science]
日期:2021-03-16
卷期号:10 (2): 680-688
被引量:3
标识
DOI:10.11591/eei.v10i2.2738
摘要
In this paper, artificial neural network is used to calibrate sensors that are commonly used in industry. Usually, such sensors have nonlinear input output characteristic that makes their calibration process rather inaccurate and unsatisfied. Artificial neural network is utilized in an inverse model learning mode to precisely calibrate such sensors. The scaled conjugate gradient (SCG) algorithm is used in the learning process. Three types of industrial sensors which are gas concentration sensor, force sensors and humidity sensors are considered in this work. It is found that the proposed calibration technique gives fast, robust and satisfactory results.
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