基于ART-2和SOFM神经网络模型的中国上市公司财务风险识别实证研究

2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics Pub Date : 2013-08-26 DOI:10.1109/IHMSC.2013.287

Guangrong Li

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引用次数: 1

摘要

本文旨在比较神经网络的自适应共振理论(ART)和自组织特征映射(SOFM)在中国上市公司财务风险识别研究中的应用。实证结果表明，ART-2神经网络模型的识别效果优于Logistic统计模型、BP和PNN网络算法，SOFM网络算法的识别效果优于ART-2。

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Empirical Study on Financial Risk Identification of Chinese Listed Companies Based on ART-2 and SOFM Neural Network Model

This paper aims at comparing Adaptive Resonance Theory ("ART" for short) and Self-organizing Feature Map ("SOFM" for short) of neural network on the study of Chinese listed company's financial risk identification. The empirical results show that the ART-2 neural network model has better recognition effect than Logistic statistical model, BP and PNN network algorithm, while the SOFM network algorithm is better than ART-2.

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2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics

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