摘要
传统的神经网络财务预警模型存在缺陷,无法解释变量间的因果关系,使训练时间增加、训练精度下降。改进后的神经网络预警模型运用了统计方法、神经网络、粗糙集和模糊技术,能够得到优于传统神经网络模型的预测结果,弥补了传统神经网络模型解释性差的缺陷,实证研究结果也证实了该模型的有效性。
The paper analyzes the limitations of the traditional neural networks which is extensively used in the financial precaution. The author makes a research on the detailed methods in improving financial precaution networks. The result of the empirical analysis proves the efficiency of the related model as well,
出处
《山西财经大学学报》
CSSCI
2007年第8期120-124,共5页
Journal of Shanxi University of Finance and Economics
关键词
财务预警
神经网络
粗糙集
financial precaution
neural networks
rough set