检索结果-南通市图书馆

The training process： Planning for strength-power training in track and field. Part 1： Theoretical aspects

Journal of Sport and Health Science 2015年第4期4卷 308-317页

作者： Brad H. DeWeese Guy Hornsby Meg Stone Michael H. Stone Exercise and Sport Science Center of Excellence Jor Sport Science and Coach Education East Tennessee State University Johnson City TN 37614 USA Kinesiology and Health Sciences Virginia Commonwealth University Richmond VA 23284 USA

The process of strength-power training and the subsequent adaptation is a multi-factorial process. These factors range from the genetics and morphological characteristics of the athlete to how a coach selects, orders, and doses exercises and loading patterns. Consequently, adaptation from these training factors may largely relate to the mode of delivery, in other words, programming tactics. There is strong evidence that the manner and phases in which training is presented to the athlete can make a profound difference in performance outcome. This discussion deals primarily with block periodization concepts and associated methods of programming for strength-power training within track and field. 2015 Production and hosting by Elsevier B.V. on behalf of Shanghai University of Sport.

关键词： Periodization Strength-power training Track and field training process

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Quantum Contextuality for training Neural Networks

引用

Chinese Journal of Electronics 2020年第6期29卷 1178-1184页

作者： ZHANG Junwei LI Zhao School of Computer Science and Technology Tianjin University Search Division Alibaba Group

In the training process of Neural networks(NNs),the selection of hyper-parameters is crucial,which determines the final training effect of the *** them,the Learning rate decay(LRD) can improve the learning speed and accuracy;the Weight decay(WD)improves the over-fitting to varying ***,the decay methods still have problems such as hysteresis and stiffness of parameter adjustment,so that the final model will be *** on the Quantum contextuality(QC)theory,we propose a Quantum contextuality constraint(QCC) to constrain the weights of nodes in NNs to further improve the training *** the simplest classification model,we combine this constraint with different methods of LRD and WD to verify that QCC can further improve the training effect on the decay *** performance of the experiments shows that QCC can significantly improve the convergence and accuracy of the model.

关键词： decay method Other topics in statistics hysteresis quantum contextuality constraint WD neural nets weight decay stiffness Data handling techniques QCC parameter adjustment simplest classification model learning speed learning rate decay quantum contextuality theory training process LRD training neural networks final training effect Neural computing techniques learning (artificial intelligence) NN pattern classification Knowledge engineering techniques hyper-parameters

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：