Abstract
The paper presents a new method for modelling of non-linear temperature sensor and correction of its dynamic errors by means of Artificial Neural Networks (ANNs). Feedforward multilayer ANNs with a moving window method and recurrent ANNs were applied. In the proposed correction technique an inverse dynamic model of the sensor is implemented by means of a neural corrector. ANN based modelling and correction technique has been evaluated experimentally for small platinum RTD immersed in oil. Recurrent ANN was used as a simulator for modelling sensor's non-linear dynamics and to validate the correction technique.
Cite
CITATION STYLE
Jackowska-Strumiłło, L. (2004). ANN based modelling and correction in dynamic temperature measurements. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3070, pp. 1124–1129). Springer Verlag. https://doi.org/10.1007/978-3-540-24844-6_176
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