Object. Development of a method for predicting a two-three dimensional velocity model of a medium by using a shear wave. Complexly structured geological medium is studied on the basis of geophysical and geological data using an artificial neural network. Method. It provides the construction and use of medium models according to geophysical well logging data and other terrestrial geophysical methods. In contrast to existing methods, the proposed method also uses additional data on the medium.
In the article the approximation of the function of wind speed changes by linear functions based on Walsh functions and the prediction of function values by linear regression method is made. It is shown that under the condition of a linear change of the internal resistance of the wind generator over time, it is advisable to introduce the wind speed change function with linear approximation. The system of orthonormal linear functions based on Walsh functions is given.
The article is devoted to the investigations of the landslide processes on the right bank of Dnipro within Kyiv reservoir. The long-term temporal prediction of landslides to 2020 based on consideration of the complex
influence of meteorological, geophysical, geological and hydrological factors of landslides development in the
time have been done.
The presented article is devoted to constructing regression models of the processes of dissolution, ion exchange and adsorption to create simulation systems in order to optimize the quality of soils
The moving average method with the 4 samples window width is used to raise the weekly forecast of the US dollar exchange rate accuracy. The non-iterative artificial neural network with the radial basis functions is used for. In the end we got the forecast error less than 1%
The IAS "Forecast" is developed for forecasting the electricity consumption in the original production conditions at PJSC "Lvivoblenergo." The statistical and neural network methods are used for the input data verification; is enhanced the space dimensions extending methods for the incoming data to use them with the ANN with non-iterative training
Reservoir Computing is a paradigm of training Recurrent Neural Networks based on treating the recurrent part (the so-called “reservoir”) differently from the readouts. This paradigm has become so popular recently due to its computational efficiency and the fact that it’s enough to train only a supervised readout. Meanwhile Evolving Systems define a new approach which focuses on learning fuzzy systems that have both their parameters and their structure adapting on-line.