引导学生积极参与数学课堂学习的探索与实践

树立新理念,创建和谐学习情境,唤醒学生的主体意识,让学生在积极的思维和情感活动中主动地参与教学过程就越来越显现出其重要性了,这也是新课程改革给我们提出的最基本的要求。本文从兴趣引路,激发学生主动参与的积极性;营造民主和谐的氛围,创造学生主动参与的环境;开展小组合作学习,构建学生主动参与的平台;体验成功的愉悦,培养学生的创新能力;正确运用评价,树立学生主动参与的信心等五个方面,阐述了在数学课堂教学中如何让学生主动参与学习。     

当代教师的基本素养

随着社会对人才培养质量要求的提高,对教师的素质有了更高的要求,要求当代教师具备教学反思的能力、终身学习的理念、执着的教育追求、高度的责任感、仁爱之心、宽容的情怀等基本素养。     

主动隔振系统的最优控制时滞

针对主动隔振系统带有时滞的鲁棒渐进稳定问题,研究了时滞的存在对最优控制算法控制效果的影响,运用MATLAB中LMI工具,采用变步长试凑的方法,解出了最优控制系统反馈控制鲁棒渐进稳定的最大允许时滞上界。最后,通过数值仿真,验证了理论的正确性。仿真结果表明:随着控制时滞的增大,主动隔振效果逐渐变差,一旦反馈控制力时滞超过最大允许时滞上界,系统便将发散。同时,增大反馈控制力和减小加权矩阵控制目标的相应权值,可以提高最大允许时滞上界。     

试论公安现役高等院校办学特色

办学特色是高等学校生存发展的前提和办学优势的集中体现,也是形成核心竞争力的关键。公安现役高等院校突出办学特色,要着力创新办学理念,强化学科专业特色和人才培养特色。     

二语习得理论在大学英语教学中的应用

如何能把英语学好,这是英语学习者最为关心的问题。而英语教育工作者关注的问题则是,如何能把教学搞好,教学工作的核心是什么。根据二语习得理论,关键是采取多种教学方式,对学生进行有效的语言输入,给学习者建立一个理想的英语习得环境。     

普通高校非英语专业新生英语学习观念和策略的研究

本文以问卷和面谈的形式对非英语专业大学一年级新生作了一次调查,通过定量和定性分析发现:(1)学生对外语学习中的个体差异,外语学习的内容和形式,母语在外语学习中的作用没有明确的看法;(2)学生普遍认同管理学习过程的重要性;(3)学生的学习观念和学习策略之间存在着很大的差距。并探讨了此调查结果对外语策略培训的启示。     

内隐学习对我国数学教学的启示

内隐学习的研究成果对我国的数学教学具有很强的启示意义,主要表现在:内隐学习揭示了数学知识的大量贮存将产生积累效应的特点,它可以有效地实现数学教学中的隐性目标,对数学概念的形成也有着巨大的影响。对学生数学学习的作用不容忽视。     

A misclassification cost‐minimizing evolutionary–neural classification approach

Machine learning algorithms that incorporate misclassification costs have recently received considerable attention. In this paper, we use the principles of evolution to develop and test an evolutionary/genetic algorithm (GA)‐based neural approach that incorporates asymmetric Type I and Type II error costs. Using simulated, real‐world medical and financial data sets, we compare the results of the proposed approach with other statistical, mathematical, and machine learning approaches, which include statistical linear discriminant analysis, back‐propagation artificial neural network, integrated cost preference‐based linear mathematical programming‐based minimize squared deviations, linear integrated cost preference‐based GA, decision trees (C 5.0, and CART), and inexpensive classification with expensive tests algorithm. Our results indicate that the proposed approach incorporating asymmetric error costs results in equal or lower holdout sample misclassification cost when compared with the other statistical, mathematical, and machine learning misclassification cost‐minimizing approaches. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006.     

基于学习矢量量化(LVQ)神经网络的雷达体制识别

由于现代战争中雷达体制的多样化、复杂化及其综合应用使得雷达体制识别中要处理大量复杂的高维数据,学习矢量量化(LVQ)神经网络不仅能处理有监督分类,而且相对于其他神经网络能以较小的计算量处理大量输入数据,所以采用LVQ对雷达体制进行识别,同时针对LVQ学习速率的变化可能引起学习算法不稳定,采用修正的学习速率算法.在简要介绍雷达体制和LVQ的基础上构造了LVQ神经网络对雷达体制进行分类.通过与径向基神经网络(RBFN)识别算法的仿真对比,证实了方法的有效性.     

Maneuvering target tracking of UAV based on MN-DDPG and transfer learning

Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles(UAVs).In this paper,aiming to address the control problem of maneuvering target tracking and obstacle avoidance,an online path planning approach for UAV is developed based on deep reinforcement learning.Through end-to-end learning powered by neural networks,the proposed approach can achieve the perception of the environment and continuous motion output control.This proposed approach includes:(1)A deep deterministic policy gradient(DDPG)-based control framework to provide learning and autonomous decision-making capa-bility for UAVs;(2)An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning;and(3)An algorithm of task-decomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV's control model built based on MN-DDPG.The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV's flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments.