利用动态学习率改进BP算法，建立了混合神经网络模型。
A mixed NN model is constructed using BP algorithm improved with dynamic learning rate.
该文研究用BP神经网络建立船舶主柴油机模型的方法，并对BP算法进行了改进，提出了一种动态优化学习率的方法。
This paper proposes a method to build up a model of a Marine main engine based on BP neural network. A new method of selecting leaning rate is also stated using dynamic optimal algorithm.
本章重点从邻域函数、学习率调整等方面研究了二维网络的改进算法，并将之应用于烟叶动态分类问题。
The algorithm of two-dimensional Kohonen network is improved from serval aspects such as neighborhood function, learning rate, etc. It is applied into tobacco clustering.
在此基础上，本文提出了一种混合的方法，同时考虑这三个因素，动态调整学习率和正则化系数。
Based on this, this paper proposes a hybrid method that simultaneously considers these three factors, and dynamically tunes the learning rate and regularization coefficient.
在此基础上，本文提出了一种混合的方法，同时考虑这三个因素，动态调整学习率和正则化系数。
Based on this, this paper proposes a hybrid method that simultaneously considers these three factors, and dynamically tunes the learning rate and regularization coefficient.
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