Ternopil National Economic University

Ternopil', Ukraine

Ternopil National Economic University, TNEU founded in 1971. It is located in the city of Ternopil, Ternopil Oblast, Ukraine.Viktor Yushchenko, the president of Ukraine , is an alumnus of TNEU. Wikipedia.

Time filter
Source Type

Bodnar D.I.,Ternopil National Economic University | Voznyak O.H.,Ternopil National Economic University | Mykhal'chuk R.I.,Kharkiv Polytechnic Institute
Journal of Mathematical Sciences (United States) | Year: 2017

An inequality for harmonic means is proved and used to establish a sufficient criterion of convergence and to estimate the rate of convergence of branched continued fractions with positive components. © 2017 Springer Science+Business Media New York

Voronych A.,Ivano Frankivsk National Technical University of Oil and Gas | Pastukh T.,Ternopil National Economic University
Proceedings of 13th International Conference: The Experience of Designing and Application of CAD Systems in Microelectronics, CADSM 2015 | Year: 2015

The use of entropy for new methods of digital signal processing and transmission is explained in this paper. © 2015 Lviv Polytechnic National University.

Jun S.,Hubei University | Kochan O.,Ternopil National Economic University
Measurement Techniques | Year: 2015

The occurrence of an error due to the thermoelectric inhomogeneity of thermocouple electrodes, acquired during prolonged use, is considered, and the properties of this error are formulated. A mathematical model of the error of chromel and alumel electrodes is constructed, the error in measuring the temperature is estimated, and the most dangerous mode of operation of the thermocouples is revealed. © 2015, Springer Science+Business Media New York.

Melnyk A.,Ternopil National Economic University
Modern Problems of Radio Engineering, Telecommunications and Computer Science, Proceedings of the 13th International Conference on TCSET 2016 | Year: 2016

This paper is dedicated to solving important scientific and technical problem which consists in evaluating the efficiency of systems for distance education. © 2016 National University Lviv Polytechnic.

Osolinskyy O.,Ternopil National Economic University
Modern Problems of Radio Engineering, Telecommunications and Computer Science, Proceedings of the 13th International Conference on TCSET 2016 | Year: 2016

This paper investigates the interference immunity of the accurate measuring system for measuring the average energy consumption of microcontrollers while executing instructions, commands, programs or fragments of programs. There are six criteria for finishing a measuring process has been invented. These criteria ensure high accuracy and interference immunity of measurement results. The dual comparator ensuring adjustment of a measuring time to the period of the power supply network has been developed. The charts of the error due to influence of interference caused power supply network of 50-Hz frequency have been given in the paper. These charts allow us to estimate the error as a function of the amplitude of interference voltage as well as deviation of time of measurements from one or a few periods of the abovementioned power supply network. It has been shown that the error of measurements is quite small even for considerable amplitudes of interference and large deviations of time of measurements from the period of the power supply network. © 2016 National University Lviv Polytechnic.

Galeshchuk S.,Ternopil National Economic University
Neurocomputing | Year: 2016

Exploration of ANNs for the economic purposes is described and empirically examined with the foreign exchange market data. For the experiments, panel data of the exchange rates (USD/EUR, JPN/USD, USD/GBP) are examined and optimized to be used for time-series predictions with neural networks. In this stage the input selection, in which the processing steps to prepare the raw data to a suitable input for the models are investigated. The best neural network is found with the best forecasting abilities, based on a certain performance measure. A visual graphs on the experiments data set is presented after processing steps, to illustrate that particular results. The out-of-sample results are compared with training ones. © 2015 Elsevier B.V.

Turchenko V.,Ternopil National Economic University
Advances in Intelligent Systems and Computing | Year: 2014

The development of parallel batch pattern back propagation training algorithm of multilayer perceptron with two hidden layers and the research of its parallelization efficiency on many-core system are presented in this paper. The model of multilayer perceptron and batch pattern training algorithm are theoretically described. The algorithmic description of the parallel batch pattern training method is presented. Our results show high parallelization efficiency of the developed algorithm on many-core parallel system with 48 CPUs using MPI technology. © Springer International Publishing Switzerland 2014.

Galeshchuk S.,Ternopil National Economic University
Advances in Intelligent Systems and Computing | Year: 2016

To study market inefficiency which comes from rapidly developing software and technological progress in whole, we introduce technological bias in the exchange-rate market. The idea of technological bias emerges from the fact that recently innovative approaches have been used to solve trading tasks and to find the best trading strategies. If we consider the same pace of technological progress of trading infrastructure and computational tools along with software in the coming years, the traders who are able to adapt to this technological changes will get more profitable trading solutions than those who will require more time to adjust. Described situation displays market inefficiencies that challenge the idea of the efficient market theory, but are in line with adaptive market hypothesis. To support our suggestion about technological bias we compare the performance of deep learning methods, shallow neural network with ARIMA method and random walk model using daily closing between three currency pairs: Euro and US Dollar (EUR/USD), British Pound and US Dollar (GBP/USD), and US Dollar and Japanese Yen (USD/JPY). The results reveal the convincing accuracy of deep neural networks comparing to the other methods demonstrating the capacity of new computational methods based on evolving software. Shallow neural network outperform random walk model that confirms the idea of market inefficiency, but cannot surpass ARIMA accuracy significantly. © Springer International Publishing Switzerland 2016.

Shushpanov D.G.,Ternopil National Economic University
Actual Problems of Economics | Year: 2016

The article outlines today’s features of food consumption by various demographic, social and economic groups (by gender, age, marital status, social status and income, place of residence) in Ukraine. As a result, it defines the risk groups of population for whom food consumption is one of major determinants of health. The study also evaluates the role of the energy value of consumed food for health of various decile population groups in Ukraine and sets the priorities in improving the availability and quality in maintaining population health. © 2016, National Academy of Management. All rights reserved.

Agency: European Commission | Branch: FP7 | Program: MC-IIFR | Phase: PEOPLE-2007-4-2.IIF | Award Amount: 15.00K | Year: 2011

The proposed research is focused on the software library development for parallel neural networks training on computational Grids. The main scientific reason of the proposed research is to develop enhanced parallel neural network training algorithms which provide better parallelization efficiency on heterogeneous computational Grids in the contrast to existing algorithms. The objectives of the proposed research are: 1. to adapt the computational cost model of parallel neural network training algorithms within single pattern, batch pattern and modular approaches to heterogeneous computational Grid resources of host institution; 2. to develop enhanced single pattern and batch pattern parallel neural network training algorithms based on improved communication and barrier functions; 3. to develop a method of automatic matching of parallelization strategy to architecture of appropriate parallel computing system; 4. to develop parallel Grid-aware library for neural networks training capable to use heterogeneous computational resources; 5. to test experimentally parallel Grid-aware library for neural networks training on heterogeneous computational Grid system of host institution within the tasks of one of its active projects; 6. to deploy parallel Grid-aware library for neural networks training on the computational Grid of return host; 7. to test experimentally parallel Grid-aware library on computational systems of both host institution and return host. The cost models of the algorithms will be developed using computational complexity approaches, improved barrier and reducing function will be adapted to neural network parallelization schemes, optimization strategies will be used to find best matching architecture of parallel system neural network parallelization scheme, software library will be implemented on C programming language and MPI parallelization, the efficiency of parallel algorithm will be assessed in comparison with sequential implementation.

Loading Ternopil National Economic University collaborators
Loading Ternopil National Economic University collaborators