Current article considers the results of the study of air and water flow temperature prediction error on the number of inputs in neural network. Authors guide the architecture of neural network for temperature prediction. The formula of temperature step response for real sensor is given. Also, the method for calculating the time constants for the temperature step response formula using real measurement data is considered.
The purpose of the present article is to study the dependence of the temperature prediction error on the number of inputs in neural network and to verify the neural network on real measurement data. First, an algorithm for creating and calculating the test sequences for neural network training is researched. Second, the neural network training this algorithm is studied for predicting the temperature on the basis of the real measurement data. The last are received in the laboratory of Institute of Process Measurement and Sensor Technology in Ilmenau University of Technology. The test equipment for air and water temperature measurements is described. The measurement of air temperature was performed with J6-type thermocouple. Air temperature measurements were performed at the air velocity 1.0, 3.0 and 5.0 m/s. For each case the temperature predictions have been fulfilled by means of the neural network with 10, 20 and 40 inputs. The table with maximal air temperature prediction error is given.
The measurement of water temperature was performed with N-type thermocouple at the water velocity of 0,2 m/s. For prediction, neural networks with 20 and 40 inputs have been used. For both cases, the prediction error was accessed as practically the same.
Conclusions about air and water temperature prediction results have confirmed that the computed results coincide with the experiments.
 F. Bernhard, Handbuch der technischen tempera-turmessung. Springer Vieweg, 2014.
 N. Yaryshev, Theoretical basis for measuring non-stationary temperature. Leningrad, USSR: Energoatomizdat, 1990.
 L. Michalski, K. Eckersdorf, J. Kucharski, J. McGhee, Temperature measurement, John Wiley & Sons, Ltd, 2001.
 N. Kovalchuk, E. Polischuk, I. Pytel, K. Semenistyi, “Modern methods and means of determining the dynamic characteristics of converters”, iss.1, TS-6, NIITEI Instrumentation, 1983.
 D. Kriesel, A Brief Introduction to Neural Networks, 2007, [Online]. Available: http://www.dkriesel.com/en/science/ neural_networks
 R. Bordawekar, B. Blainey, R. Puri, Analyzing Analytics. Morgan & Claypool Publishers, 2015.
 O. Lopatko, I. Mykytyn, “Temperature value prediction errors using neural networks and ideal transition process”, Measuring equipment and metrology, no.78, p.20-24, 2017.
 S. Augustin, T. Fröhlich, M. Schalles, S. Krummeck, “Bilateral comparison for determining the dynamic characteristic values of contact thermometers in fluids”, Journ. sensors & sensor syst., 2018. [Online]. Available: https://doi.org/10.5194/jsss-7-331-2018.
 F. Lieneweg, Übergangsfunktion (Anzeigeverzögerung) von Thermometern – Aufnahmetechnik, Meßergebnisse, Auswertungen, Archiv für Technisches Messen, 1964, R46–R53, 1964.
 H. Mammen, G. Krapf, C. Hoffmann, T. Sasiuk, M. Pufke, S.Marin, T. Fröhlich, “Prüfeinrichtung zur Untersuchung des dynamischen Verhaltens von Berührungsthermometern in Wasser”, in Proc. TEMPERATUR 2017, 17 and 18 Mai 2017, PTB Berlin, Tagungsband, 2017, pp.163–168.