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Neural Computing and Applications

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01.11.2010 | Original Article

Analysis of international debt problem using artificial neural networks and statistical methods

verfasst von: Berna Yazici, Memmedaga Memmedli, Atilla Aslanargun, Senay Asma

Erschienen in: Neural Computing and Applications | Ausgabe 8/2010

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Abstract

It is known from the scientific researches that artificial neural networks are alternatives of statistical methods such as regression analysis and classification in recent years. Since multi-layer backpropagation neural network models are nonlinear, it is expected that the neural network models should make better classifications and predictions. The studies on this subject support that idea. In this study, a macro-economic problem on rescheduling or non-rescheduling of the countries’ international debts is taken into account. Among the statistical methods, logistic and probit regression, and the different neural network backpropagation algorithms are applied and comparisons are made. Evaluations and suggestions are made depending on the results and different neural network architecture.

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Titel: Analysis of international debt problem using artificial neural networks and statistical methods
verfasst von: Berna Yazici
Memmedaga Memmedli
Atilla Aslanargun
Senay Asma
Publikationsdatum: 01.11.2010
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 8/2010
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-010-0422-4

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