审计指标评价识别的Rough-ANN模型

Study on Rough - ANN Model of Audit Indicators Evaluation

由于审计指标体系的开放性,当需要甄别的指标结构相对简单的时候,比较容易处理,但是当面对更加复杂的的财务报表以及其他相关数据时,需要完善和建立的审计指标系统也会更加复杂。本文首先介绍了Rough集(粗糙集)和ANN(人工神经网络)理论的出现为审计指标识别带来的机会,分析了这种识别模型的确立的可能性,将Rough集和ANN相结合,构造了审计对象识别的Rough-ANN模型,并结合一个实例来验证此模型的可行性和有效性。

Audit indicator is an open system, so when the structure is relatively simple, it is relatively easy to handle, but when the structure is more complex with financial statements and other relevant data, the need to improve the system of indicators will be more complex. This paper introduces the Rough set and ANN （artificial neural network） theory and the opportunities they bring to the audit indicator model. And the model will combine Rough sets and ANN to structure the evaluation audit Rough-ANN model, and then the paper explains an example to verify the feasibility and effectiveness of this model.