Design of a high precision temperature measurement system based on artificial neural network for different thermocouple types
Design of a high precision temperature measurement system based on artificial neural network for different thermocouple types
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DOI：
10.1016/j.measurement.2006.03.015
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发表时间：
2006-10-01
期刊：
影响因子：
5.6
通讯作者：
Celebi, F. V.
中科院分区：
文献类型：
--
作者：
Danisman, K.;Dalkiran, I.;Celebi, F. V.
许多类型的传感器本质上是非线性的，但需要线性的输出。如果接受线性近似，对于给定的精度水平，噪声和测量误差总是存在的。因此，经常需要曲线拟合技术来平均这些影响。估计传感器的输入输出特性的问题越来越多地使用软件技术来解决。本文介绍了一种实验方法的非线性估计，测试和校准的不同类型的热电偶使用人工神经网络（ANN）的算法集成在一个虚拟仪器（VI）。分别采用人工神经网络和带有信号调理单元的数据采集卡进行数据优化和实验数据采集。在人工神经网络的训练和测试阶段，Wavetek 9100校准单元用于获得实验数据。人工神经网络成功完成训练后，它将被用作神经线性化器，根据热电偶的输出电压计算温度。(C)2006爱思唯尔有限公司保留所有权利。
Many types of sensors are nonlinear in nature but require an output that is linear. If linear approximation is accepted, for a given accuracy level, noise and measurement errors are always present. Therefore, curve-fitting techniques are frequently required to average these effects. The problem of estimating the sensor's input-output characteristics is being increasingly tackled using software techniques. This paper describes an experimental method for the estimation of nonlinearity, testing and calibrating of the different thermocouple types using artificial neural network (ANN) based algorithms integrated in a virtual instrument (VI). An ANN and a data acquisition board with designed signal conditioning unit are used for data optimization and to collect experimental data, respectively. In both training and testing phases of the ANN, the Wavetek 9100 calibration unit is used to obtain experimental data. After the successful training completion of the ANN, it is then used as a neural linearizer to calculate the temperature from the thermocouple's output voltage. (C) 2006 Elsevier Ltd. All rights reserved.