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.
This paper is about finding the optimal route of mobile robots. A genetic algorithm was analyzed and created on the basis of neural net used in this task.
Запропоновано методи і критерії оптимізації архітектури нейронної мережі, призначеної для розв’язання актуальних задач з захисту інформації в комп’ютерних системах та мережах. Наведено приклади оптимізації архітектури нейронної мережі, призначеної для виявлення комп’ютерних вірусів.