Abstract
The paper presents new methods for modelling of temperature sensors’ dynamics by means of Artificial Neural Networks (ANN) and hybrid analytical-neural approach. Feedforward multilayer ANN and a moving window method, as well as Recurrent Neural Networks (RNN) are applied. The proposed modelling techniques were evaluated experimentally for two small platinum Resistance Temperature Detectors (RTDs) immersed in oil. Experiments were performed in temperature range, for which the sensors characteristics are nonlinear. The proposed ANN-based and hybrid analytical-neural models were validated by means of computer simulations on the basis of the quality of dynamic errors correction. It was shown that in the process conditions for which classical methods and linear models fail, the application of ANNs and hybrid techniques which combine soft and hard computing paradigms can significantly improve modelling quality.
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References
Michalski, L., Eckersdorf, K., McGhee, J.: Temperature Measurement. John Wiley and Sons Ltd., Chichester (1991)
Jackowska-Strumillo, L., Sankowski, D., McGhee, J., Henderson, I.A.: Modelling and MBS experimentation for temperature sensors. Measurement 20(1), 49–60 (1997)
Cimerman, F., Blagojevic, B., Bajsic, I.: Identification of the dynamic properties of temperature sensors in natural and petroleum gas. Sensors & Actuators A 96, 1–13 (2002)
Minkina, W.: Theoretical and experimental identification of the temperature sensor unit step response non-linearity during air measurement. Sensors & Actuators A 78, 81–87 (1999)
Jackowska-Strumiłło, L.: ANN based modelling and correction in dynamic temperature measurements. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 1124–1129. Springer, Heidelberg (2004)
Xie, W.F., Zhu, Y.Q., Zhao, Z.Y., Wong, Y.K.: Nonlinear system identification using optimized dynamic neural network. Neurocomputing 72, 3277–3287 (2009)
Mantovanelli, I.C.C., Rivera, E.C., Da Costa, A.C., Filho, R.M.: Hybrid Neural Network Model of an Industrial Ethanol Fermentation Process Considering the Effect of Temperature. Applied Biochemistry and Biotechnology 817, 136–140 (2007)
Narendra, K.S., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. IEEE Trans. on Neural Networks 1(1), 4–27 (1990)
Jackowska-Strumiłło, L.: Correction of non-linear dynamic properties of temperature sensors by the use of ANN. In: Rutkowski, L., Kacprzyk, J. (eds.) Advances in Soft Computing – Neural Networks and Soft Computing, pp. 837–842. Physica-Verlag, Heidelberg (2003)
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Jackowska-Strumillo, L. (2011). Hybrid Analytical and ANN-Based Modelling of Temperature Sensors Nonlinear Dynamic Properties. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21219-2_45
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DOI: https://doi.org/10.1007/978-3-642-21219-2_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21218-5
Online ISBN: 978-3-642-21219-2
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