一种改进的BP算法

ＢＰ算法是目前应用极为广泛的神经网络算法,但它也存在一些不足。文中提出了采用共轭梯度法及黄金分割相结合的改进ＢＰ算法（ＭＢＰ）,自适应调整学习率,提高了运算速度,减少了迭代次数。最后将标准ＢＰ算法和ＭＢＰ算法进行了比较,仿其结果表明：ＭＢＰ算法的学习次数和收敛速度得到极大改善。     

基于多尺度稀疏字典的SAR图像目标识别方法

针对合成孔径雷达目标识别问题,提出一种基于多尺度稀疏字典的SAR图像目标识别方法。稀疏字典选择是稀疏表示中的关键问题之一,该方法利用小波多尺度分析构造过完备稀疏字典,将训练样本图像在小波解析域中进行小波多层分解,充分利用小波多尺度分析突出图像局部特征的特点,并和过完备稀疏表示有效结合组成级联字典。通过求解测试样本相应的稀疏系数矢量并根据系数矢量中对应训练样本类别的重构误差判定目标类型。实验结果表明,该方法在识别前无需对SAR图像进行预处理,具有良好的识别效果。     

Allocation of quality improvement targets based on investments in learning

Purchased materials often account for more than 50% of a manufacturer's product nonconformance cost. A common strategy for reducing such costs is to allocate periodic quality improvement targets to suppliers of such materials. Improvement target allocations are often accomplished via ad hoc methods such as prescribing a fixed, across‐the‐board percentage improvement for all suppliers, which, however, may not be the most effective or efficient approach for allocating improvement targets. We propose a formal modeling and optimization approach for assessing quality improvement targets for suppliers, based on process variance reduction. In our models, a manufacturer has multiple product performance measures that are linear functions of a common set of design variables (factors), each of which is an output from an independent supplier's process. We assume that a manufacturer's quality improvement is a result of reductions in supplier process variances, obtained through learning and experience, which require appropriate investments by both the manufacturer and suppliers. Three learning investment (cost) models for achieving a given learning rate are used to determine the allocations that minimize expected costs for both the supplier and manufacturer and to assess the sensitivity of investment in learning on the allocation of quality improvement targets. Solutions for determining optimal learning rates, and concomitant quality improvement targets are derived for each learning investment function. We also account for the risk that a supplier may not achieve a targeted learning rate for quality improvements. An extensive computational study is conducted to investigate the differences between optimal variance allocations and a fixed percentage allocation. These differences are examined with respect to (i) variance improvement targets and (ii) total expected cost. For certain types of learning investment models, the results suggest that orders of magnitude differences in variance allocations and expected total costs occur between optimal allocations and those arrived at via the commonly used rule of fixed percentage allocations. However, for learning investments characterized by a quadratic function, there is surprisingly close agreement with an “across‐the‐board” allocation of 20% quality improvement targets. © John Wiley & Sons, Inc. Naval Research Logistics 48: 684–709, 2001     

模型不可知的联合相互学习

主流的联邦学习(federated learning, FL)方法需要梯度的交互和数据同分布的理想假定,这就带来了额外的通信开销、隐私泄露和数据低效性的问题。因此,提出了一种新的FL框架,称为模型不可知的联合相互学习 (model agnostic federated mutual learning, MAFML)。MAFML仅利用少量低维的信息(例如,图像分类任务中神经网络输出的软标签)共享实现跨机构间的“互学互教”,且MAFML不需要共享一个全局模型,机构用户可以自定制私有模型。同时,MAFML使用简洁的梯度冲突避免方法使每个参与者在不降低自身域数据性能的前提下,能够很好地泛化到其他域的数据。在多个跨域数据集上的实验表明,MAFML可以为面临“竞争与合作”困境的联盟企业提供一种有前景的解决方法。     

军校大学生英语学习观念与策略分析

国内外学者提出了许多关于英语学习策略的理论,并进行了大量的实证研究,但有关军队院校大学生英语学习策略的研究尚未涉猎。了解军队院校非英语专业大学生英语学习的观念和所采用的主要策略,掌握学生各种英语学习观念和采用的学习策略之间的关系。可为军校英语教学提供研究方向。     

军校战士学员厌学现象思考

学风是学生群体在学习活动中表现出来的精神风貌,包括正确的学习目的、严谨的治学态度、求实创新的精神、浓厚的学习氛围和浓郁的文化环境。战士学员厌学现象,妨碍院校教育终极目标的实现,必须引起高度关注。     

内隐学习的特征及对其本土教育的启示

内隐学习就是无意识获得刺激环境中复杂知识的过程。在无意识研究热潮中,内隐学习研究获得了很大的发展。内隐学习的研究对于理解人类认识过程的本质,特别是对于探明人们获得丰富复杂知识的心理机制提供了全新的视野。本文着重探讨内隐学习的特征以及对本土教育的启示。     

英语教学中自主学习能力的培养

自主学习是语言学习的关键,学生是否能够学好外语取决于自主学习能力的培养。本文阐述了自主学习的理论基础及使用价值,如何培养学生自主学习的能力,更多关注学习方法的相互作用。     

初中生学习动机与自我监控学习行为关系研究

采用问卷调查法对新疆石河子市初中生的学习动机及自我监控学习行为进行测查,并分析其相关性,结果表明：不同性别、年级初中生的四种学习动机强度有差异,自我监控学习行为水平也有显著差异;成就性动机、威信性动机、认知性动机与自我监控学习行为均有显著正相关,附属性动机与其无明显相关。     

素质教育下课堂教学互动平台的建立

教学过程的改革是教育改革的主要内容,课堂教学是教学活动的主要环节之一。课堂教学要适应素质教育的要求,建立民主平等合作的师生关系,达到教与学的互动,提升课堂教学的效果。