Analysis and Simulation of the Early Warning Model for Human Resource Management Risk Based on the BP Neural Network

Wei Wang,Xue Yan,Shouheng Sun,Xiangwu Deng

doi:10.1155/2020/8838468

Abstract

Human resource management risks are due to the failure of employer organization to use relevant human resources reasonably and can result in tangible or intangible waste of human resources and even risks; therefore, constructing a practical early warning model of human resource management risk is extremely important for early risk prediction. The back propagation (BP) neural network is an information analysis and processing system formed by using the error back propagation algorithm to simulate the neural function and structure of the human brain, which can handle complex and changeable things that do not have an obvious linear relationship between output results and input factors, so as to find the objective connection between the two. Based on the summary and analysis of previous research works, this article expounded the research status and significance of early warning for human resource management risks, elaborated the development background, current status, and future challenges of the BP neural network, introduced the method and principle of the BP neural network’s connection weight calculation and learning training, performed the risk inducement analysis, index system establishment, and network node selection of human resource management, constructed an early warning model of human resource management risk based on the BP neural network, conducted the risk warning model training and detection based on the BP neural network, and finally carried out a simulation and its result analysis. The study results show that the early warning model of human resource management risk based on the BP network is effective, and this trained and tested BP network risk warning model can be used to conduct early warning empirical research on human resource risks to prevent human resource risks, ensure enterprise’s benign operation, and at the same time play a role in supervision and promotion of market order rectification.

Highlights

Human resources have become an indispensable key resource for the development of enterprises, and enterprises have gradually realized its role and importance and raised it to an unprecedented height
Due to the inherent special attributes of human resources, the risks of human resource management are more concealed than other business risks and are often overlooked [3]. erefore, constructing a practical early warning model of human resource management risk is extremely important for early risk prediction, and risk management plays a pivotal role in the entire management system of an enterprise
Under the guidance of organizational vision and organizational goals, human resource management grasps the demand and supply of human resources from a strategic height according to the characteristics of human resource activities, dynamically plans human resources, and strives to balance human resource demand and supply, thereby promoting organizational goals achieve. e node selection of the back propagation (BP) neural network has the following characteristics: the organization of human resource management activities is highly compatible with the business strategy; the organization’s horizontal and vertical organization settings match the needs of internal and external human resource management behaviors; employees at all levels within the

Summary

Introduction

Human resources have become an indispensable key resource for the development of enterprises, and enterprises have gradually realized its role and importance and raised it to an unprecedented height. Based on the summary and analysis of previous research works, this article expounded the research status and significance of early warning for human resource management risks, elaborated the development background, current status, and future challenges of the BP neural network, introduced the method and principle of the BP neural network’s connection weight calculation and learning training, performed the risk inducement analysis, index system establishment, and network node selection of human resource management, constructed an early warning model of human resource management risk based on the BP neural network, conducted the risk warning model training and detection based on the BP neural network, and carried out a simulation and its result analysis. E study results of this paper will provide a reference for further research studies on the analysis and simulation of the early warning model of human resource management risk based on the BP neural network.

Methods and Principles

Evaluation system warning

Simulation and Result Analysis

Conclusion and Future