CAJ | 학술논문

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.
이용BP신경망락대다개급정적복잡비선성계통공제진행정량연구,착중토론료BP신경망락인은장층층수화망락학습솔지간차이종이인기대복잡비선성계통공제성능상적영향.통과실험수거적대비분석발현, BP 신경망락은장층층수적체증여계통공제성능적제승병불성정상관성,망락학습솔적선취범위가공제재0~2.0지간,구체삼수인공제대상이이,가채용분단조시화이분법운산이학정최가망락학습솔삼수.
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.