An Integrated Model of Rough Set and Radial Basis Function Neural Network for Early Warning of Enterprise Human Resource Crisis

Abstract

Early warning of whether an enterprise will be faced with human resource crisis is a new hotspot in the study of enterprise crisis. This study contributes to early warning of enterprise human resource crisis by proposing an integrated model of Rough Set (RS) and Radial Basis Function (RBF) neural network, which overcomes the shortcomings of long training time and complex network structure in the traditional neural network methods. The proposed model fully exerts the advantages of the two methods of RS and RBF neural network. By means of RS for attribute reduction, the input data are reduced but still reflects the main information of the original data. And RBF neural network has simple network structure, strong nonlinear approximation ability, and fast convergence speed. First, this study sets up the enterprise human resource crisis early-warning index system. Second, 55 training samples are trained to construct the human resource crisis early-warning model, and 5 testing samples are used to test the forecasting effect of the model. Finally, this study compares the performance of RS–RBF neural network to those of Back Propagation (BP) neural network and RBF neural network and RS-BP neural network. The model comparison results show that the proposed model simplifies the structure of the neural network, speeds up the learning speed of the network, and improves forecasting efficiency and accuracy, which can give early warning of enterprise human resource crisis more effectively.