College Employment Quality Prediction Method Based on BP Neural Network
Tao Yong-hong · 2014
This paper presents a college employment quality prediction method based on BP neural network. Structure of the BP neural network is given in advance, which includes input layer, hidden layer and output layer. To implement the college employment quality prediction, "number of college graduates", "number of graduates with employment intentions", "number of graduates who have jobs" and "survey data" are utilized as the input. The main ideas of this paper lie in that we combine particle swarm optimization and BP neural network together to forecast the college employment quality. College employment quality prediction results can be obtained from the output layers of the BP neural network when the ending conditions are satisfied. To make performance evaluation, experiments are conducted. Experimental results show that the proposed algorithm is effective to forecast the college employment quality.