基于粗糙集和神经网络的上海最低工资标准研究

Research on standard of lowest wage of Shanghai with RS and NN

介绍了粗糙集基本理论和基于粗糙集的神经网络建模,根据粗糙集理论的属性约简步骤,提出了融合粗糙集对原有的神经网络模型加以改进的分析研究方法,并将此方法应用于对上海最低工资标准的分析研究,给出了应用此方法进行实证研究的过程和分析结果,并给出了用约简后的属性进行学习过程时的误差分析曲线。旨在保留重要信息的前提下,消除多余的属性数据,提高仿真的精度和速度,从而更好地为政府制定相应决策,提供更科学合理的依据。

Rough set （RS） theory and neural networks （NN） model with RS are introduced. Based on reduction process in RS, an analyzing method which is better than NN model is put forward. This new method is applied in research on the lowest wage of Shanghai. Also, the results and error curves are given. The analytic course proves that this method can get important data and decrease the redun- dancies. The results with better precision provide criteria for govemment to make policy.