The present article considers neural networks as a tool for the temperature prediction using transition process. The authors emphasize the need to measure high temperatures in technological processes and indicate problems encountered on this way. The method proposed to solve this problem is neural networks application. The study of artificial neural networks is motivated by their similarity to successfully working biological systems, which – in comparison to the overall system – consist of very simple but numerous nerve cells that work massively in parallel and (which is probably one of the most significant aspects) have the capability to learn. There is no need to explicitly program a neural network. One result from this learning procedure is the capability of neural networks to generalize and associate data: after successful training a neural network can find reasonable solutions for similar problems of the same class that were not explicitly trained. This in turn results in a high degree of fault tolerance against noisy input data. At the very beginning the authors describe artificial neuron as a basis of a neural network and provide its block diagram. Neurons classification depending on the functions they perform in the neural network is also present. They also defined the transfer function of the artificial neuron and its basic types (linear transfer function, positive linear transfer function, piecewise linear transfer function, step transfer function and logistic transfer function) alongside with mathematical expressions (formulas) and diagrams that describe neural networks behavior. Then, the authors present a neural networks classification based on their architecture (feedforward neural networks, recurrent neural networks and completely linked neural networks were specially highlighted). Each type of these was provided with detailed drawings and structures explanation. In addition, the present article includes a neural network classification, based on training algorithm and the type of problem that such neural network is able to perform. At the end of the article the authors make conclusions about the most relevant neural network architecture in case of temperature prediction problem using transition process and consider the corresponding learning algorithm. Plans for further research were also outlined.

1. Alexander von Beckerath, Anselm Eberlein, Hermann Julien, Peter Kersten, Jochem Kreutzer, WIKA Handbook Pressure & Temperature Measurement. – Cumming: Corporate Printers, 2008. – 423 p. 2. Ярышев Н. А. Теоретические основы измерения нестационарной температуры. – 2-е изд., перераб. – Л.: Энергоатом- издат, 1990. – 256 с. 3. Каллан Р. Основные концепции нейронных сетей / пер. с англ. А. Г. Сивака. – М.: Вильямс, 2001. – 287 с. 4. Уоссермен Ф. Нейро- компьютерная техника: Теория и практика / пер. с англ. Ю. А. Зуев, В. А. Точенов. – 1992. – 184 с. 5. Круглов В. В., Борисов В. В. Искусственные нейрон- ные сети. Теория и практика. – 2-е изд. – М.: Горячая линия-Телеком, 2002. – 382 с. 6. Kriesel D. A Brief Introduction to Neural Networks, 2007, http://www. dkriesel.com/en/science/neural_networks 7. Rajesh Bordawekar, Bob Blainey, Ruchir Puri, Analyzing Analytics. – Morgan & Claypool Publishers, 2015. – 124 p. 8. Осовский С. Нейронные сети для обработки информации / пер. с польского И. Д. Рудинский. – М.: Финансы и статистика, 2002. – 344 с. 9. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, An introduction to statistical learning. – Springer Science+Business Media New York, 2013. – 426 p.