改进的BP神经网络在元器件建模中的应用

Application of Progressed Back-propagation Neural Network to Modeling Electron Devices

提出一种基于BP神经网络的非线性电子元器件建模的新方法。以具体实验数据为例,以MATLAB中的神经网络工具箱为工具,采用了改进的BP神经网络,并对其设计方案进行了详细的分析说明,发现动量参数对训练次数影响很大,而学习率对它的影响很小;采用双隐含层比单隐含层训练更稳定,收敛的也更快速,同时给出了理想的学习方案。最后通过实验验证了该方法的有效性。

Based on the Back-propagation Neural Network theory, a new method is presented to model nonlinear electron devices. By the sample applications, an optimized project of Back-propagation training is introduced by using Neural Network toolbox in MATLAB software, and the project is explained in detail and the good learning scheme is given by simulating the experimental results of the concrete experimental results. In the method, the momentum parameter α has intensive influence on the training times, while the learning ratio η has little effect on them. In addition, the training is more effective with the couple hidden layers than that with the single hidden layer. Finally, it is proved that the modeling method is accurate and converged quickly by the experiment.