Neural neworks in a management information systems

Weinlichová, Jana; Štencl, Michael

doi:10.11118/actaun200957060331

Cited by 2 publications

(4 citation statements)

References 2 publications

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“…This is why neural models are now included in most standard statistical software packages [15]. Weinlichova and Stencl [17] came with the possibility of applying artificial intelligence by means of neural networks. They found an particular, thereby characterizing the importance of knowledge management.…”

Section: Introductionmentioning

confidence: 99%

“…They also find that neural networks are often better than linear models, but only for short-term prognoses [18]. Weinlichova and Stencl [17] admit there are other possibilities for data processing apart from the tried and tested methods i.e. managerial information systems and business intelligencenamely by means of neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…managerial information systems and business intelligencenamely by means of neural networks. Weinlichova and Stencl [17] monitor and describe this possibility in detail and compare it with existing possibilities.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang, Patuwo and Hu [18] suggest that scientists are not completely sure about the influence of key factors for predictions on the performance of artificial neural works. Weinlichova and Stencl [17] present several possible applications of neural networks to various types of tasks in a managerial information system.…”

Section: Introductionmentioning

confidence: 99%

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The Application of Artificial Neural Networks on the Prediction of the Future Financial Development of Transport Companies

Vochоzka¹,

Sheng²

2016

Komunikácie

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It is logical that in an uncertain economic environment it is necessary for companies to be able to plan better, to report more precisely and to be able to better evaluate their future financial development. For their analysis, the authors of this article focus on a particular segment of the national economy, namely transport and freight forwarding companies.The aim of this article is to utilize artificial neural networks to predict potential financial problems in transport companies in the Czech Republic.Data on all companies involved in transport and freight forwarding in the Czech Republic for the period 2003 -2013 were used for modelling the particular neural network. The data file contained nearly 15,000 records on companies for the individual years. These records included both financial statement data and non-accounting data (e.g. data on company employees).The following networks were used for modelling the neural network: a linear network, a probabilistic neural network (PNN), a generalised regression neural network (GRNN), a radial basis function network (RBF), a three-layer perceptron network (TLP) and a four-layer perceptron network (FLP).The analysis resulted in a concrete model of an artificial neural network. The neural network is able to determine with more than ninety per cent accuracy whether a company is able to overcome potential financial problems, within how many years a company might go bankrupt, or whether a company might go bankrupt within one calendar year. The text also includes the basic statistical characteristics of the examined sample and the achieved results (sensitivity analysis, confusion matrix, etc.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Application of Artificial Neural Networks on the Prediction of the Future Financial Development of Transport Companies

Vochоzka¹,

Sheng²

2016

Komunikácie

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Comparison of time series forecasting with artificial neural network and statistical approach

Štencl¹,

Popelka²,

Šťastný³

2014

Acta Univ. Agric. Silvic. Mendelianae Brun.

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Mendel. Brun., 2011, LIX, No. 2, pp. 347-352 In this paper we concentrate on prediction of future values based on the past course of a variable. Traditionally this problem is solved using statistical analysis -fi rst a time-series model is constructed and then statistical prediction algorithms are applied to it in order to obtain future values. The time series modelling is a very powerful method, but it requires knowledge or discovery of initial conditions when constructing the model. The experiment described in this paper consists of a comparison of results computed by Multi-layer perceptron network with diff erent learning algorithms previously published and results computed with diff erent types of ARMA models. For the network confi guration an analytical approach has been applied through the cross-validation method. We performed an exact comparison of both approaches on real-world data set. Results of two types of artifi cial neural network learning algorithms are compared with two algorithms of statistical prediction of future values. The experiment results are later discussed from several diff erent points. First the comparison is focused on output precision of both approaches. The comparison consists of matching neural networks results and real values on few steps of prediction. Then the results of ARMA models are compared with real values and conclusion is made. The conclusion also includes theoretical and practical recommendations.artifi cial neural networks, time series forecasting, statistical approach, comparison studyThe aim of the article is comparison of real data forecast using statistical methods and artifi cial neural network (ANN). Traditionally, diff erent methods of statistical analysis are fi xed part of the decision process in the economical resolution (Husek, 2007) (Meloun, Militký, 2004). An artifi cial neural network off ers a kind of intelligent automation of the decision process where the variants of solutions are prepared. Also the principle of ANN is diff erent to statistical models. The ANNs are much less sensitive to input conditions. These points justify the increasing number of successful real-world applications of ANN in business.ANN is one of the Artifi cial Intelligence (AI) methods. These are used to solve tasks where the standard approach is not eff ective or impossible. The main areas of AI applications are forecasting, classifi cation and optimization. Actual applications of ANN include the usage in Management Information systems (Wenlichová, Štencl, 2009), or classification in evaluation of consumer behaviour (Wenlichová, Fejfar, 2010). In a broader application area, the implementation of various machine-learning algorithms as part in development of search engines for geodetic data (as published by Procházka (2010)).The comparison of methods described in this article is related to a previously published paper (Štencl, Šťastný, 2010), which was focused on optimization of the ANN's learning process. Both papers are part of a comparison study focused on forecasting of

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Neural neworks in a management information systems

Cited by 2 publications

References 2 publications

The Application of Artificial Neural Networks on the Prediction of the Future Financial Development of Transport Companies

The Application of Artificial Neural Networks on the Prediction of the Future Financial Development of Transport Companies

Comparison of time series forecasting with artificial neural network and statistical approach

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