摘要
企业财务危机预测是非线性预测,各个影响因素之间又存在着复杂的组合决策关系,并且现实中的数据多为连续的,很难直接用于机器分类学习。因此文中从分析财务预警问题的特点出发,融合了智能软计算的多种方法建立完整的预测模型。首先以粗糙集决策表一致性水平、区间平均信息熵、离散化程度等因素为离散化结果的评价标准;然后利用遗传算法全局、并行搜索的优点,以上面提到的3个因素作为启发信息对所有条件属性的割点集合进行最优搜索。得到离散化的数据后,用BP神经网络对数据进行分类学习。最终网络学习训练后对企业财务状况进行了预测,实验结果表明:系统的预测正确率达93%。
Enterprise＇s financial crisis predicts is the non- linear prediction, there is a complicated association decision relation between each influence factor,and the data in reality are continuous, it is very difficult to be used in the categorized machine to study directly. After analyzing the characteristic of the early warning problem, merged many kinds of soft computing methods to construct the prediction model. Firstly, take consistency level of decision, average information entropy and degree of discretization as evaluation criteria of the result of diseretization. Then utilize the overall search of genetic algorithm to find the optimized cut points. After diseretization by the optimized cut points,tram the BP neural network with the samples in training set. When finishing the training,use the BP neural network to predict financial crisis of listed company, and the experimental result indicates,the prediction rate is up to 93%.
出处
《计算机技术与发展》
2008年第11期100-102,共3页
Computer Technology and Development
基金
国家自然科学基金(60473142)
安徽工业大学科研项目(200704)
关键词
财务预警
粗糙集
遗传算法
神经网络
financial early - warning
rough set
genetic algorithm
neural network
