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首页> 外文期刊>Journal of International Money and Finance >A new approach to modeling early warning systems for currency crises: Can a machine-learning fuzzy expert system predict the currency crises effectively?
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A new approach to modeling early warning systems for currency crises: Can a machine-learning fuzzy expert system predict the currency crises effectively?

机译：为货币危机预警系统建模的新方法：机器学习模糊专家系统能否有效预测货币危机？

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摘要

This paper presents a hybrid causal model for predicting the occurrence of currency crises by using the neuro fuzzy modeling approach. The model integrates the learning ability of the neural network with the inference mechanism of fuzzy logic. The empirical results show that the proposed neuro fuzzy model leads to a better prediction of crisis. Significantly, the model can also construct a reliable causal relationship among the variables through the obtained knowledge base. Compared to neural network and the traditionally used techniques such as logit, the proposed model can thus lead to a somewhat more prescriptive modeling approach based on determinate causal mechanisms towards finding ways to prevent currency crises.
机译：本文提出了一种混合因果模型，用于通过使用神经模糊建模方法来预测货币危机的发生。该模型将神经网络的学习能力与模糊逻辑的推理机制相结合。实证结果表明，所提出的神经模糊模型可以更好地预测危机。重要的是，该模型还可以通过获得的知识库在变量之间构建可靠的因果关系。与神经网络和传统使用的诸如logit之类的技术相比，所提出的模型可以基于确定的因果机制导致某种更具说明性的建模方法，以寻找预防货币危机的方法。

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