基于遗传算法优化的BP神经网络在考研结果预测中的应用

Application of BP Neural Network Based on Genetic Algorithms Optimization in Prediction of Postgraduate Entrance Examination

通过遗传算法先对BP神经网络的初始权值和阈值进行优化后,再将BP神经网络用于考研结果的预测模型中。实验表明,这种优化后的预测模型因为克服了收敛速度慢、易产生局部最小等缺陷,比单纯使用BP神经网络建立的预测模型准确度更高。将这个预测模型用于考研报名之前供学生预测参考,方便学生做出合理的决策,具有一定的实际意义。

Firstly,the initial weight and threshold of BP neural network are optimized by genetic algorithm,and then BP neural network is used in the prediction model of the results of the postgraduate entrance examination.The experiment shows that the optimized prediction model overcomes the shortcomings of slow convergence speed and easy to produce local minimum,so it is more accurate than the prediction model established by BP neural network alone.This prediction model can be used as a reference for students to make a reasonable decision before applying for postgraduate entrance examination.