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

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 shou...

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Veröffentlicht in:	Neural computing & applications Jg. 19; H. 8; S. 1207 - 1216
Hauptverfasser:	Yazici, Berna, Memmedli, Memmedaga, Aslanargun, Atilla, Asma, Senay
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer-Verlag 01.11.2010 Springer
Schlagworte:	Applied sciences Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer science; control theory; systems Data Mining and Knowledge Discovery Exact sciences and technology Image Processing and Computer Vision Inference from stochastic processes; time series analysis Learning and adaptive systems Linear inference, regression Mathematics Multivariate analysis Original Article Probability and statistics Probability and Statistics in Computer Science Sciences and techniques of general use Statistics Conjugate gradient method Logistic and probit regression Backpropagation algorithm Quasi-Newton method Artificial neural network Rescheduling and non-rescheduling of the international debts Backpropagation Evaluation Neural computation Statistical analysis Prediction Regression analysis Neural network Economic sciences Neural net architecture Statistical method Statistical regression Logistic regression Analysis method Classification Multilayer network
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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