General Regression Neural Networks in Forecasting the Scales of Higher Education

摘要：

The historical scales of higher education of a given area can be viewed as a time series which is charactered by uncertainty, nonlinearity and time-varying behavior. Predictions for the number of enrolled students in colleges of Shandong province of China and its modified data were carried out respectively by means of General Regression Neural Network (GRNN) forecasters. The detailed designs for architectures of GRNN models, transfer functions of the hidden layer nodes, input vectors and output vectors were made with many tests. Experimental results show that the performance of GRNN for forecasting the scales of the near future scales of higher education is acceptable and effective.

作者: LIU Zhao-cheng LIU Xi-yu ZHENG Zi-ran WANG Gong-xi

作者单位: School of Management and Economics, Shandong Normal University, Jinan,250014,China Department of Man School of Management and Economics, Shandong Normal University, Jinan,250014,China Department of Management, Jinan Railway Institute of Technology, Jinan,250013,China

会议类型: 国际会议

会议名称: 2009 IEEE International Symposium on IT in Medicine & Education( IEEE 教育与医药信息化国际会议)

会议地点: 济南

会议语种:英文

页码: 1257-1261

在线出版日期: 2009-08-14（万方平台首次上网日期，不代表论文的发表时间）