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.
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.
In this article is considered the algorithm of logistic regression and construction of the neural network for the recognition of handwritten symbols in the image. Examples of implementation of two approaches for solving the problem of numerical recognition are given. The efficiency of using a neural network, as the provision of the most reliable recognition results, is explored.
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.
A variant of consistent application of the compressive neural network and context modeling techniques for efficient data compression, including images and audio signals is being viewed. The method is based on the representation of intermediate storage archive in a fixedpoint format and provides improved performance of compression coefficient and quality of primary data reproduction.
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 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).
This article provides the comparison of libraries neural networks. Based on this analysis was determined to develop a neural network for classification of spectra based on Encog library, because it implemented many components and gives the best result with a small number of items for training. Showed the architecture of neural networks for data classification of spectral analysis.
The oil extraction process requires continuous monitoring of the oil well equipment operation. One of the most effective methods of the on-line control of sucker-rod pumps operation is obtaining information from the force sensor in the polished rod or the current sensor of the pump jack driving motor. In many cases, timely troubleshooting and preventive repair allow saving large costs.
The paper analyzes the main trends of search the basic direction and identifying the priority of electronic documents handling in the Internet. The technical principles of construction of information retrieval system and ability to use linguistic processor and neural networks for solving problems determining the importance of consideration the input documents were grounded. The object of the study was the decision support systems designed to determine priority of review incoming documents.