Postgraduate Entrant and Employment Forecasting Using Modified BP Neural Network with PSO

摘要：

It is hard to train the influence variables and to forecast the complex problems due to the time series. Recently the neural network method has been successfully employed to solve the forecasting problem. In this paper, an approach that integrate modified BP neural network optimized with particle swarm optimization algorithm (MBPPSO) is proposed which applied to forecast postgraduate entrant and employment problem. It introduces particle swarm optimization algorithm to optimize the initial weights of the BP neural network, which effectively improve velocity of convergence BP neural network. Moreover, the adaptive adjust learn strategy is introduced to avoid acutely shake of train and decrease the bias error. The experiment results show MBPPSO can achieve reasonable forecast result.

关键词： particle swam optimization BP neural network postgraduate entrant and employment forecasting

作者: Xianjun Shen Caixia Chen Tingting He Jincai Yang

作者单位: Department of Computer Science Central China Normal University Wuhan, China

会议类型: 国际会议

会议名称: 第四届国际计算机新科技与教育学术会议(2009 4th International Conference on Computer Science & Education)

会议地点: 南京

会议语种:英文

页码: 1699-1703

在线出版日期: 2009-07-25（万方平台首次上网日期，不代表论文的发表时间）