Artificial neural networks provide powerful models for solving many economic classifications, as well as regression problems. For example, they were successfully used for the discrimination between healthy economic agents and those prone to bankruptcy, for the inflation-deflation forecasting, for the currency exchange rates prediction, or for the prediction of share prices. At present, the neural models are part of the majority of standard statistical software packages. This paper discusses the basic principles, which the neural network models are based on, and sum up the important principles that must be respected in order that their utilization in practice is efficient.
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AGRIS - 国际农业科技情报系统
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Agricultural Economics (AGRICECON)
卷
57
期
3
页码
150
- 157
ISSN
0139-570X | 1805-9295
出版者
Czech Academy of Agricultural Sciences
其它主题
Neural network training; Time series prediction; Bankruptcy prediction; Multilayer neural networks; Error functions
语言
英语
2024-12-11
DOAJ