本文提出了以粗糙集与神经网络相结合的技术方法,应用于我国上市公司财务危机预警研究中。在通过以中国上市公司财务数据为基础进行实证分析之后,结果表明粗糙集的引入减少了神经网络的输入维数,采用动量添加法和参数自适应算法修正的神经网络算法,在网络训练的准确性和精度上都优于传统的BP神经网络。

A method which combines the rough set and ANN is proposed, and it is applied to the financial risk research of Chinese listed companies. By empiric analyzing on the financial date of Chinese listed companies, the results show that the introduction of the rough set cuts down the input dimension of ANN, and the ANN algorithm is improved by the momentum accession and the parameter self-adaptation. The modified algorithm has advantages over the traditional BP ANN in the veracity and precision of network training.