Fitting transducer characteristics to measured data
Fitting transducer characteristics to measured data
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DOI：
10.1109/5289.975463
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发表时间：
2001-12-01
影响因子：
2.1
通讯作者：
Postolache, O
中科院分区：
文献类型：
--
作者：
Pereira, JMD;Girao, PMBS;Postolache, O
没有规则可以为给定的数据集选择最佳的曲线拟合方法。这个问题在测量应用中非常重要。优化传感器特性插值或线性化的模拟和数字方法是一个不断进行研究的领域，特别是因为智能传感器的自动校准和自测试是人们主要关注的主题。我们概述了数据插值和最小均方回归的经典方法。我们对多项式和人工神经网络逼近测量数据的相对性能进行了比较评估，特别注意减少所需的校准集维度以获得给定的精度。
There are no rules to select the best curve-fitting method for a given set of data. This problem is of great importance in measurement applications. Optimizing analog and digital methods for a transducer's characteristic interpolation or linearization is a field where constant research is being done, particularly since auto-calibration and self-test of intelligent transducers is a topic of major interest. We present an overview of classical methods for data interpolation and least mean squares regression. We make a comparative evaluation of the relative performance of polynomial and artificial neural networks approximations to measurement data with particular attention paid to the reduction of the required calibration set dimension to obtain a given accuracy.