Based on the idea of standard back-propagation (BP) learning algorithm, an improved BP learning algorithm is presented.
在标准反向传播神经网络算法的基础上，提出了一种改进的反向传播神经网络算法。
Based on expatiated the basic structure model and some general improved algorithms of BP neural network, this paper brings forward a new self-organization learning algorithm.
介绍了BP网络的基本结构模型与常见改进算法，在此基础上提出了一种新型的结构自组织BP网络算法。
This paper presents an improved BP algorithm, which can adapt learning rate using gold-segmentation.
本文提出了一种改进的BP算法，该算法基于黄金分割法自适应调整网络学习速率。
A mixed NN model is constructed using BP algorithm improved with dynamic learning rate.
利用动态学习率改进BP算法，建立了混合神经网络模型。
An improved BP algorithm and a learning sample generation method based on statistics are performed.
提出了一种基于统计的学习样本生成方法，使样本生成问题规范化。
An improved BP algorithm and a learning sample generation method based on statistics are performed.
提出了一种基于统计的学习样本生成方法，使样本生成问题规范化。
应用推荐