1) weight optimal initialization
初始权值优化
1.
Furthermore, a weight optimal initialization method is introduced for improving performance of the soft-sensing model.
针对回归神经网络训练效率低,泛化能力差等问题,尝试引入一种初始权值优化方法加以改进。
2) optimization of initial weights and threshold values
初始权值和阈值优化
3) optimal weights initialization technology
初始权值优化技术
1.
Also a novel optimal weights initialization technology is proposed so that the sample sets and initial weights can match perfectly.
针对移动机器人建立了基于BP神经网络的智能避障控制模型,提出了初始权值优化技术,使得样本组与初始权值相匹配,显著地提高了网络的收敛速度。
2.
Based on multiplayer feed-forward neural networks using BP algorithm, considering the fluctuation of initial weights and sample sets, a novel optimal weights initialization technology is proposed for the sake of matching between initial weights and sample sets.
本文在采用BP学习算法的多层感知器的基础上,考虑初始权值的波动和样本组的因素,提出了初始权值优化技术,以提高初始权值与样本组的匹配能力。
4) optimized initial value
优化初始值
1.
Modeling variance is used to find the optimized initial values with the method of minimum modeling variation in GM(1,1) model.
依据建模方差2δ最小的原则,对传统GM(1,1)模型的初始值进行改进,提出基于优化初始值的GM(1,1)模型。
5) weight initialization
权值初始化
1.
A novel learning algorithm is proposed that is based on the combination of independent component analysis(ICA)based weight initialization and automatically adjusting the gain parameter of sigmoid activation function.
提出了一种基于独立元分析(ICA)方法的权值初始化方法和动态调整S型激励函数的斜率相结合的神经网络学习算法。
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参考词条