BP神经网络在企业财务预警中的应用

根据预警系统体系指标的要求,对企业财务预警指标进行了筛选。采用BP神经网络预测与Adaboost算法相合的分析方法,对采集的样本数据建立了强分类器并进行了分类误差分析研究,完成了公司财务预警系统分析。结果表明:用BP_Adaboost分类算法建立的强分类器的分类误差率低于仅用BP算法建立的弱分类器的分类误差率,此法具有可行性和一定的计算精度,能为企业财务预警提供一定的理论指导。

Application of BP Neural Network in Enterprise Finance Early Warning

According to the requirement of early warning system,the financial early warning parameters were selected.By means of BP Adaboost method,one strong classifier for the collected data was established and the classification error was analyzed.In the end,the analysis of the company’s financial early warning system was completed.The results show that the classification error of the strong classifier established with BP Adaboost method is lower than that of the weak classifier established only with BP method.As a result,the method has a certain feasibility and accurateness,and this can provide some theoretical guidance for enterprises’ financial early warning.