BP神经网络用于函数逼近的最佳隐层结构

研究采用反向传播算法的人工神经网络用于函数逼近时的支结构。方法，以典型的ｎ输入、单输出的多层ＢＰ网为例，在几种不同的网络隐层结构下对典的连续函数进行逼近训练。，分析各网络输出的全局误差。

The Most Suitable Architecture of Hidden - Layer in BP Neural Networks for Function Approximation

Aim To determine the most suitable architecture of hidden-layer in an er- ror-back -propagation neural netwok for function approximation. Methods To train the typical multi-layer BP neural netwoks with different hidden layers and neurons to approximate a typical function, and analyze the results. Results The most suitable number of hidden layers in a BP neural network is 4,and the most suitable number of neurons in each hidden layer is between 10 to 20,and a BP neural network with a single hidden layer has the worst results.Conclusion For function approximation,the most suitable number of hidden layers in a BP neural network should be about 4, and there should be suitable number of neurons in each hidden layer.