scholarly journals Analysis of Transportation Companies in the Czech Republic by the Kohonen Networks – Identification of Industry Leaders

2020 ◽  
Vol 23 (1) ◽  
pp. A32-A43
Author(s):  
Jakub Horák ◽  
Petr Šuleř ◽  
Jaromír Vrbka
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Czech Republic ◽  
Computational Models ◽  
Data File ◽  
Transportation Industry ◽  
The Czech Republic ◽  
Kohonen Networks ◽  
Artificial Neural ◽  
Accuracy Of Results

Computational models of artificial neural networks are currently used in different areas. Accuracy of results exceeds the performance of traditional statistical techniques. Artificial neural networks as the Kohonen map may be used e.g. to identify industry leaders, thus replacing the traditional cluster analysis and other methods. The aim of this contribution is to analyse the transportation industry in the Czech Republic by the Kohonen networks and identify industry leaders. The data file contains results - division of companies into a total of 100 clusters. Each cluster is subjected to analysis of absolute indicators and several parameters, average, as well as absolute, are examined. In total, 88 firms may be considered as industry leaders. Consequently, a fairly small group of companies has a strong influence on development of the whole transportation industry in the Czech Republic.

scholarly journals Analysis of business companies based on artificial neural networks

2019 ◽  
Vol 61 ◽  
pp. 01013 ◽  
Author(s):  
Veronika Machová ◽  
Marek Vochozka
Keyword(s):  
Neural Networks ◽  
Developing Countries ◽  
Artificial Neural Networks ◽  
Czech Republic ◽  
National Economy ◽  
Driving Force ◽  
Number Of Clusters ◽  
The Czech Republic ◽  
Artificial Neural

Business companies have many kinds of products that they sell to other businesses, consumers, etc. They are a driving force of economies, especially in developing countries. The aim of this article is to analyse business companies in the Czech Republic using artificial neural networks and subsequently to estimate the development of this branch of the national economy. An analysis is performed to create a significant number of clusters of businesses. An analysis of the most significant clusters is also carried out. The result can be generalized and we can predict the number of companies that will be creditworthy or bankrupt in the following period. This makes it possible to estimate not only the overall growth or decline of business companies in the Czech Republic, but also to estimate the structure of the companies in terms of their size, turnover or volume of sales.

scholarly journals PREDICTION OF FINANCIAL DISTRESS: CASE OF MINING ENTERPRISES IN CZECH REPUBLIC

2020 ◽  
Vol 15 (1) ◽  
pp. 1-14
Author(s):  
Zuzana Rowland ◽  
Alla Kasych ◽  
Petr Suler
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Czech Republic ◽  
Financial Stability ◽  
Grey Zone ◽  
Financial Health ◽  
Classification Problems ◽  
Mining Companies ◽  
Artificial Neural

The ability to predict a company's financial health is a challenge for many researchers and scientists. It is also a distracting topic, as many other new approaches to financial health predictions have emerged in recent years. In this paper, we focused on identifying the financial health of mining companies in the Czech Republic. We chose the neural network method because, based on various instances of related research, neural networks represent a more reliable financial forecast than mathematical-statistical methods such as discriminant analysis and logistic regression. The concept of a neural network emerged with the first artificial neural networks, inspired by biological systems. The existence of prediction and classification problems directly predetermines artificial neural networks with respect to a given issue. We used the Amadeus database for processing, including financial indicators, SPSS, and Visual Gene Developer software. In total, we analyzed sixty-four mining companies. Complete data on financial stability were available for fifty-three companies, which we explored, and based on these results, identified financial situations for the other thirteen. Based on the available information, we processed a neural network and regression analysis. We managed to classify thirteen companies as solvent, insolvent, and in the grey zone, with the help of prediction.

scholarly journals Metamodel for nodal pressure estimation at near real-time in water distribution systems using artificial neural networks

2017 ◽  
Vol 20 (2) ◽  
pp. 486-496 ◽  
Author(s):  
Gustavo Meirelles Lima ◽  
Bruno Melo Brentan ◽  
Daniel Manzi ◽  
Edevar Luvizotto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Distribution Systems ◽  
Water Distribution ◽  
Computational Models ◽  
Pressure Sensors ◽  
Water Supply Systems ◽  
Pressure Data ◽  
Proposed Model ◽  
Artificial Neural

Abstract The development of computational models for analysis of the operation of water supply systems requires the calibration of pipes' roughness, among other parameters. Inadequate values of this parameter can result in inaccurate solutions, compromising the applicability of the model as a decision-making tool. This paper presents a metamodel to estimate the pressure at all nodes of a distribution network based on artificial neural networks (ANNs), using a set of field data obtained from strategically located pressure sensors. This approach aims to increase the available pressure data, reducing the degree of freedom of the calibration problem. The proposed model uses the inlet flow of the district metering area and pressure data monitored in some nodes, as input data to the ANN, obtaining as output, the pressure values for nodes that were not monitored. Two case studies of real networks are presented to validate the efficiency and accuracy of the method. The results ratify the efficiency of ANN as state forecaster, showing the high applicability of the metamodel tool to increase a database or to identify abnormal events during an operation.

Evolutionary Approaches for ANNs Design

2011 ◽  
pp. 575-580
Author(s):  
Antonia Azzini ◽  
Andrea G.B. Tettamanzi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Algorithms ◽  
Computational Models ◽  
Artificial Neurons ◽  
Important Research Topic ◽  
Recent Developments ◽  
Evolving System ◽  
Artificial Neural ◽  
Ann Application

Artificial neural networks (ANNs) are computational models, loosely inspired by biological neural networks, consisting of interconnected groups of artificial neurons which process information using a connectionist approach. ANNs are widely applied to problems like pattern recognition, classification, and time series analysis. The success of an ANN application usually requires a high number of experiments. Moreover, several parameters of an ANN can affect the accuracy of solutions. A particular type of evolving system, namely neuro-genetic systems, have become a very important research topic in ANN design. They make up the so-called Evolutionary Artificial Neural Networks (EANNs), i.e., biologicallyinspired computational models that use evolutionary algorithms (EAs) in conjunction with ANNs. Evolutionary algorithms and state-of-the-art design of EANN were introduced first in the milestone survey by Xin Yao (1999), and, more recently, by Abraham (2004), by Cantu-Paz and Kamath (2005), and then by Castellani (2006). The aim of this article is to present the main evolutionary techniques used to optimize the ANN design, providing a description of the topics related to neural network design and corresponding issues, and then, some of the most recent developments of EANNs found in the literature. Finally a brief summary is given, with a few concluding remarks.

scholarly journals Enhancing the reusability and interoperability of artificial neural networks with DEVS modeling and simulation

2016 ◽  
Vol 07 (02) ◽  
pp. 1650005 ◽  
Author(s):  
David Ifeoluwa Adelani ◽  
Mamadou Kaba Traoré
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Modeling And Simulation ◽  
Computational Models ◽  
Learning Algorithm ◽  
Discrete Event ◽  
Neural Network Modeling ◽  
System Specification ◽  
Ann Models ◽  
Artificial Neural

Artificial neural networks (ANNs), a branch of artificial intelligence, has become a very interesting domain since the eighties when back-propagation (BP) learning algorithm for multilayer feed-forward architecture was introduced to solve nonlinear problems. It is used extensively to solve complex nonalgorithmic problems such as prediction, pattern recognition and clustering. However, in the context of a holistic study, there may be a need to integrate ANN with other models developed in various paradigms to solve a problem. In this paper, we suggest discrete event system specification (DEVS) be used as a model of computation (MoC) to make ANN models interoperable with other models (since all discrete event models can be expressed in DEVS, and continuous models can be approximated by DEVS). By combining ANN and DEVS, we can model the complex configuration of ANNs and express its internal workings. Therefore, we are extending the DEVS-based ANN proposed by Toma et al. [A new DEVS-based generic artficial neural network modeling approach, The 23rd European Modeling and Simulation Symp. (Simulation in Industry), Rome, Italy, 2011] for comparing multiple configuration parameters and learning algorithms and also to do prediction. The DEVS models are described using the high level language for system specification (HiLLS), [Maïga et al., A new approach to modeling dynamic structure systems, The 29th European Modeling and Simulation Symp. (Simulation in Industry), Leicester, United Kingdom, 2015] a graphical modeling language for clarity. The developed platform is a tool to transform ANN models into DEVS computational models, making them more reusable and more interoperable in the context of larger multi-perspective modeling and simulation (MAS).

Research on the Development and Applications of Artificial Neural Networks

2014 ◽  
Vol 556-562 ◽  
pp. 6011-6014 ◽  
Author(s):  
Xiao Guang Li
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intelligent Control ◽  
Computational Models ◽  
Bayesian Probability ◽  
Control Techniques ◽  
Central Nervous Systems ◽  
Artificial Neural ◽  
The Brain

Intelligent control is a class of control techniques that use various AI computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms. In computer science and related fields, artificial neural networks are computational models inspired by animals’ central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected “neurons” that can compute values from inputs by feeding information through the network. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition.

scholarly journals Recent Advancements in Study of Effects of Nano Micro Additives on Solid Propellants Combustion by Means of the Data Science Methods

2019 ◽  
Vol 69 (1) ◽  
pp. 20-26 ◽  
Author(s):  
Victor S. Abrukov ◽  
Alexander N. Lukin ◽  
Darya A. Anufrieva ◽  
Charlie Oommen ◽  
V. R. Sanalkumar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Kinetic Parameters ◽  
Data Science ◽  
Computational Models ◽  
Science Methods ◽  
Solid Propellants ◽  
Rocket Propellants ◽  
Artificial Neural ◽  
Solid Rocket

The efforts of Russian-Indian research team for application of the data science methods, in particular, artificial neural networks for development of the multi-factor computational models for studying effects of additive’s properties on the solid rocket propellants combustion are presented. The possibilities of the artificial neural networks (ANN) application in the generalisation of the connections between the variables of combustion experiments as well as in forecasting of “new experimental results” are demonstrated. The effect of particle size of catalyst, oxidizer surface area and kinetic parameters like activation energy and heat release on the final ballistic property of AP-HTPB based propellant composition has been modelled using ANN methods. The validated ANN models can predict many unexplored regimes, like pressures, particle sizes of oxidiser, for which experimental data are not available. Some of the regularly measured kinetic parameters extracted from non-combustion conditions could be related to properties at combustion conditions. Results predicted are within desirable limits accepted in combustion conditions.

scholarly journals Prediction of sea ice concentration using artificial neural networks

2021 ◽  
Author(s):  
Mohammed Kandil El-Emam El-Diasty
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Models ◽  
Ocean Model ◽  
Performance Comparison ◽  
Eastern Canada ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Artificial Neural

Artificial neural networks are computational models capable of solving complex problems through learning, or training, and then generalizing the network solution for other inputs. This thesis examines the performance of two neural network-based models, which were developed for predicting the ice concentration in the Gulf of St. Lawrence in Eastern Canada. The first is a batch model which uses time to predict future ice concentration, while the second model predicts the ice concentration sequentially. It is shown that the performance of the two models is almost identical, as long as no abrupt changes occur in the ice conditions. If, however, the ice condition changes suddenly, only the sequential model is proved to be capable of predicting the ice condition without noticeable accuracy degradation. A performance comparison is made between the developed neural network model and coupled ice-ocean model for ice concentration prediction to further validate the model.

Sign in / Sign up

Export Citation Format

Share Document