| With the continuous development and progress of society,more and more enterprises human resources data appeared.How to extract information from these human resource data has become an urgent need in the development and construction of enterprises,and data mining technology brings new opportunities for the forecast of human resource demand.Through a good human resources mining and forecasting technology can help business managers a clear understanding of the status of human resources,to provide the right human resource demand forecasting data to help managers better management of enterprises.Based on the deep understanding of the forecasting mining technology,the multiple linear regression method is determined.Through the modeling,the statistical index analysis and the significance test,the data mining algorithms are analyzed and compared.The regression equation is obtained,and the demand forecast is made on the number of the enterprise personnel,and the feasibility of the multiple regression model is verified.This paper presents a new method to improve the learning rate of BP neural network by studying the merits,processes and shortcomings of the traditional BP neural network algorithm.The method adopts the different learning rate to realize the weight between different nodes.Dynamic adjustment,so as to speed up the adjustment of the weight of speed,improve the training convergence rate.In this paper,the BP neural network algorithm with improved learning rate is described in detail,and the optimization procedure and process of the algorithm are analyzed in detail,and an example is given.Finally,the design and implementation of the human resource demand forecasting system is completed.Through the database design and module design,the main function modules of human resource management and human resources forecast are realized.Through the human resources data management and demand forecast,And finally realize the visualization of prediction results. |
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