Analysis of Enterprise Human Resources Demand Forecast Model Based on SOM Neural Network

Abstract

Human resource planning is the prerequisite of human resource management, and the basic work of human resource planning is to predict human resource demand. Scientific and reasonable human resource demand forecasting results can provide important data support for enterprise human resource planning and strategic decision-making so that human resources management can play a better role in the realization of corporate goals. Because human resource demand is affected by many factors, there is a high degree of nonlinearity and uncertainty between each factor and personnel demand, as well as the incompleteness and inaccuracy of corporate human resource data. In this paper, the self-organizing feature mapping (SOM) artificial neural network prediction model is selected as the prediction model, and the input and output process of sample data is converted into the optimal solution process of the nonlinear function. In the application of the model, the human resource demand prediction index system is used as the input of the SOM neural network and the total number of employees in the enterprise is used as the output so that the problem of nonlinear fitting between human resource demand-influencing factors and human resource demand can be solved. Finally, through the empirical analysis of the enterprise, the model forecasting process is explained and the human resource demand forecast is realized.

Figure 1

SOM network structure.

Figure 2

Neuron interaction mode.

Figure 3

The specific process of the SOM clustering algorithm.

Figure 4

Human resource demand forecasting system diagram.

Figure 5

Plot of historical data of indicators.

Figure 6

SOM model predicted values of key indicators.

Figure 7

Error of SOM neural network.

Figure 8

Linear regression of SOM neural network.

Figure 9

SOM neural network prediction results.