BP神经网络学习效率研究

Study on BP Neural Network Learning Efficiency

为了对BP神经网络学习效率进行研究,利用一个特定的半球面方程案例,对隐层单元数、传递函数、测试集样本点数这三个变量对BP网络学习效率影响程度进行分析。通过3次实验,实验一只改变一个变量、实验二同时改变两个变量,实验三同时改变三个变量来比较实验的运行时间,实验误差,记录实验结果,并进行了实验分析,得出BP神经网络学习效率最佳时的参数取值。结果表明当隐层单元数为8、传递函数为tansig、测试集样本点数为16时,BP神经网络学习效率最佳。

In order to study the learning efficiency of BP neural network,a specific case of hemispherical equation was used to analyze the impact of three variables,ie,the number of hidden layer units,the transfer function and the number of test sample points on the learning efficiency of BP network.After three experiments,one variable was changed in experiment one,two variables were changed in experiment two and three variables were changed in experiment three at the same time to compare the running time and experimental error of the experiment.The experimental results were recorded and analyzed experimentally to get BP The parameter value of the neural network to learn the best efficiency.The results show that BP neural network has the best learning efficiency when the number of hidden layer units is 8,the transfer function is tansig,and the number of test samples is 16.