Wyniki wyszukiwania - BazTech

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Neural network modelling of non-prosperity of Slovak companies

Durica Marek, Mazanec Jaroslav, Frnda Jaroslav

Engineering Management in Production and Services

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2023

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Vol. 15, no. 3

1--13

EN

Early identification of potential financial problems is among important companies’ risk management tasks. This paper aims to propose individual and ensemble models based on various types of neural networks. The created models are evaluated based on several quantitative metrics, and the best-proposed models predict the impending financial problems of Slovak companies a year in advance. The precise analysis and cleaning of real data from the financial statements of real Slovak companies result in a data set consisting of the values of nine potential predictors of almost 19 thousand companies. Individual and ensemble models based on MLP and RBF-type neural networks and the Kohonen map are created on the training sample. On the other hand, several metrics quantify the predictive ability of the created models on the test sample. Ensemble models achieved better predictive ability compared to individual models. MLP networks achieved the highest overall accuracy of almost 89 %. However, the non-prosperity of Slovak companies was best identified by RBF networks created by the boosting and bagging technique. The sensitivity of these models is about 87 %. The study found that models based on neural networks can be successfully designed and used to predict financial distress in the Slovak economy.

2

Anomalous and traditional diffusion modelling in SOM learning

Hrebik Radek, Kukal Jaromir

Archives of Control Sciences

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2019

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Vol. 29, No. 4

699--717

EN

The traditional self organizing map (SOM) is learned by Kohonen learning. The main disadvantage of this approach is in epoch based learning when the radius and rate of learning are decreasing functions of epoch index. The aim of study is to demonstrate advantages of diffusive learning in single epoch learning and other cases for both traditional and anomalous diffusion models. We also discuss the differences between traditional and anomalous learning in models and in quality of obtained SOM. The anomalous diffusion model leads to less accurate SOM which is in accordance to biological assumptions of normal diffusive processes in living nervous system. But the traditional Kohonen learning has been overperformed by novel diffusive learning approaches.

3

Graphical analysis of load pattern in Polish Power System using PCA and Kohonen map

Siwek K.

Przegląd Elektrotechniczny

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2010

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R. 86, nr 1

118-120

EN

This work presents the graphical analysis of the load patterns of the Polish Power System by using two kinds of multidimensional decompositions - statistical PCA and the neural Kohonen map. Presented results of analysis can be directly used to the construction of the prediction model of the electroenergetic load in both short and long time perspectives.

PL

Praca przedstawia dogłębną analizę obciążeń Polskiego Systemu Elektroenergetycznego przy użyciu dwóch rodzajów dekompozycji wielowymiarowych – statystycznej PCA i neuronowej – mapy Kohonena. Przedstawione wyniki można użyć bezpośrednio do zbudowania systemu predykcji obciążeń elektroenergetycznych – zarówno krótko jak i długoterminowej.

4

Zastosowanie samoorganizujących się map cech w diagnostyce silników o zapłonie samoczynnym

Klimkiewicz M.

Inżynieria Rolnicza

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2008

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R. 12, nr 7(105)

101-108

PL

Wykorzystano właściwości samoorganizujących się map cech w wykrywaniu uszkodzeń silników z zapłonem samoczynnym. Zbudowano model, w którym zmiennymi wejściowymi są symptomy zaobserwowane przez użytkownika wskazujące na niewłaściwą pracę silnika oraz sprawdzenia i pomiary wykonane przez mechanika. Za pomocą mapy topologicznej zlokalizowano podobne skupienia przypadków. Neuronom radialnym mapy nadano etykiety zgodne z nazwami mogących się pojawić usterek.

EN

The researchers made use of self-organizing properties of maps of characteristics in detecting defects of self-ignition engines. A model was developed with the following input variables: the symptoms observed by user that indicate abnormal engine work, and checks and measurements carried out by a mechanic. Similar concentrations of clusters were located using a topological map. Radial neurons in the map were marked with labels consistent with names of defects, which may possibly occur.