基于几种神经网络方法的公司财务风险判别研究

摘要笔者针对提高公司财务风险判别准确度问题,提出运用人工神经网络中具有明显自适应性、自组织性的自适应共振理论算法和自组织特征映射算法分别构建公司财务风险判别模型进行仿真研究。结果表明自适应共振理论算法判别正确率为87%,而自组织特征映射算法判别正确率则达到了89%,较反向传播神经网络等方法判别准确度更高。

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	李光荣
	李风强

关键词：财务风险风险判别神经网络自适应共振理论自组织特征映射财务风险风险判别神经网络自适应共振理论自组织特征映射

Abstract： This paper aims at finding a method to improve the accuracy degree of company’s financial risk identification. The author puts forward the adaptive resonance theory algorithm and the self-organizing feature map algorithm, which has obvious features of adaptability and self-organizing, respectively to build the company’s financial risk discriminant model for the simulation research. The results show that the recognition accuracy of Adaptive Resonance Theory neural network reaches 87%, and Self-organizing Feature Map network algorithm reaches 89%, both of which have better recognition effects than other Artificial Neural Networks such as BP network algorithm, ect.

Keywords： Financial Risk Risk Identification Neural Network Adaptive Resonance Theory Self-organizing Feature Map Financial Risk Risk Identification Neural Network Adaptive Resonance Theory Self-organizing Feature Map

收稿日期 2017-03-30; 接受日期 2017-03-30;

基金资助:国家自然科学基金项目(71561002);国家民族委员会科研项目(14BFZ020);宁夏中小企业研究所科研项目(ZXS2015001);国家民族委员会经济管理重点开放实验室项目

作者简介: 李光荣(1980— ),男,宁夏彭阳人,博士,讲师,硕士生导师,主要从事风险管理、投融资创新等方面的研究;李风强(1969— ),男,山东潍坊人,博士,讲师,主要从事金融风险和管理工程研究。

引用本文:

李光荣,李风强.基于几种神经网络方法的公司财务风险判别研究[J]. 经济经纬, 2017,2: 122-127

LI Guang-rong, LI Feng-qiang.Company Financial Risk Identification Study Based on Several Artificial Neural Networks[J] Economic Survey, 2017,V34(2): 122-127

链接本文:

http://www.jjjw.org.cn/CN/ 或 http://www.jjjw.org.cn/CN/Y2017/V34/I2/122