1) rule learning and producing
规则学习和产生
2) learning rule
学习规则
1.
Discovering New Neural Network Learning Rule Based on Genetic Programming;
基于GP的神经网络学习规则的发现
2.
The weights of the principal component neural networks are trained with the aid of the algorithm of generalized Hebbian learning rule, and the input vectors of the local spatial features and image pixels value are transformed into feature vectors which are once clustered by Kmeans classifier, the road surface and unroad surface can be distinguish.
该方法用广义Hebb学习规则训练主元神经网络权值,然后将局部统计特征和图像像素值输入主元神经网络得到图像特征矢量,最后用K 均值分类器对该矢量进行分类,通过参考区域识别道路。
3.
In this neural network training, the author applied an error back-propagation gradient descending search technique successfully, and then proposed a new learning rule for multiple pattern pairs fuzzy associative memories in single FAM and its efficiency has been confirmed by computer imitations.
本文给出了模糊联想记忆神经网络联想输出误差函数的一种梯度计算方法,成功地把误差逆传播梯度下降搜索技术应用于该网络的训练中,提出一种新的多模式对模糊联想记忆的学习规则,计算机模拟表明该规则是十分有效的。
3) rules learning
规则学习
1.
The rules are made up by the method of rules learning,which can strengthen the portability of the system.
采用学习的方式来进行规则的生成,这种规则学习的方式使系统的可移植性大大增强。
4) rule learning
规则学习
1.
Based on the in-deep research on inductive learning theory, a rule learning algorithm is applied in building the intrusion detection model.
在对归纳学习理论深入研究的基础上,将规则学习算法应用到入侵检测建模中。
5) learning rules
学习规则
1.
Research new IF model and learning rules;
新IF模型及其学习规则研究
2.
The paper introduces the development history and features of the artificial neural network(ANN), summarizes the basic theory and learning rules of ANN, and cornments on the actualities,existing problems and tendency of its applications in oil & coal supervision of power plant chemistry.
介绍人工神经网络的发展及特点，对神经网络基础理论及各种学习规则和模型进行了概述，并且评述了目前人工神经网络在电厂化学油煤监督应用的现状、存在的问题及发展趋势。
3.
In this paper, the learning rules of MFNNs are proposed and their convergence properties are studied.
在此基础上 ,提出了单体模糊神经网络 (MFNNs)的学习规则并进一步研究了其收敛性 。
6) generation rule
产生规则
补充资料：部分学习与整体学习
部分学习与整体学习
part learning and whole learning
部分学习与整体学习(part learningand whole learning)在运动学习和记忆学习中，根据对学习内容的处理方式可以分成部分学习和整体学习。部分学习就是将材料分成几个部分，每次学习一个部分:整体学习就是每次学习整个材料。一般来讲，整体学习的效果优于部分学习。但是，课题复杂彼此没有意义联系的材料，用部分学习的效果好:课题简短或具有意义联系的材料，用整体学习的效果好。在进行学习时，可以将部分学习与整体学习结合起来，先进行整体学习再进行部分学习，或者相反。这种相互结合的学习方式叫做综合学习，效果更好些。 (周国帕撰成立夫审)
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条