прогнозування значення температури

DEPENDENCE OF NEURAL NETWORKS TEMPERATURE PREDICTION ERROR ON MEASUREMENT ERROR

The  current  article  describes  the  results  of  the  study  of  the  neural  networks  temperature  prediction  error dependence  on  measurement  errors,  which  are  random,  nonlinear  and  multiplicative  errors.  It  is  noted  applicability  of  the architecture of neural network for temperature prediction. The formula of temperature step response for ideal sensor is given. 

WATER AND AIR FLOWS TEMPERATURE PREDICTION USING NEURAL NETWORK

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.

DEPENDENCE OF TEMPERATURE VALUE PREDICTION ERROR BY NEURAL NETWORKS ON ADC RESOLUTION

Current article describes the results of the study of the error of temperature values prediction using neural networks. In the introduction, the authors consider previous research pointing out problems that arise during measuring the high temperatures. To solve these problems the neural networks applies. The formula for temperature transition process is derived.

Neural networks as a tool for the temperature value prediction using transition process

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.

Temperature value prediction errors using neural networks and ideal transition process

The present article describes the results of the study of the prediction error of temperature values using neural networks. In the introduction, the authors point out problems that arise (come up) during the measurement of high temperatures. The method proposed to solve these problems is neural networks application. At the very beginning 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).