摘要
利用BP神经网络对多个给定的复杂非线性系统控制进行定量研究，着重讨论了BP神经网络因隐藏层层数和网络学习率之间差异从而引起对复杂非线性系统控制性能上的影响．通过实验数据的对比分析发现，BP神经网络隐藏层层数的递增与系统控制性能的提升并不成正相关性，网络学习率的选取范围可控制在0～2．0之间，具体参数因控制对象而异，可采用分段调试和二分法运算以确定最佳网络学习率参数．
In this paper, quantitative study on a given complex nonlinear system is presented using BP neural network. The correlation is explored between the control performance on the given complex nonlinear system and the number of hidden layers and the difference of network learning rate. The analytical and test results well indicate that the improvement of control performance is not proportional to the increased number of hidden layers, and the optimal selection of neural learning rate is generally within 0-2. The specific value of learning rate depends on the specifically given system and the most suitable value range can be determined using sub-debugging method.
出处
《宁波大学学报（理工版）》
CAS
2013年第1期48-52,共5页
Journal of Ningbo University：Natural Science and Engineering Edition
基金
宁波市自然科学基金(2010A610109)
关键词
BP神经网络
隐藏层层数
网络学习率
多项式拟合
BP neural network
the number of hidden layers
network learning rata
curve fitting
