基于人工神经网络的高考扩招趋势预测研究

高考扩招系统足一个“黑箱”,各因素问是否存在因果关系还很模糊,基于人工神经网络自身的特点,采用BP模型,借助MATLAB语言对高考扩招系统进行预测研究．通过对历史数据的处理、程序的设计、网络的训练等进行分析,最终预测出中国未来20年高等教育发展的趋势,为国家相关政策的制定提供参考依据．     

Topography of acoustic response characteristics in the midbrain inferior colliculus of Kunming mouse

Topography of acoustic response characteristics of the midbrain inferior colliculus(IC)of the Kunming mouse was studied by using extracellular recording techniques.The characteristic frequency(CF)range represented in the different divisions of the IC differed markedly:4-15 kHz in the dorsal cortex(DC),10-70 kHz in the central nucleus(CN),and 4-35kHz in the external cortex(EC).The CF in the CN increased from dorsal and lateral to ventral and medial,higher CFs represented at its ventromedial part and lower CFs at its dorsal part.The isofrequency contours of CFs were incurvate.Minimum thresholds(MT)of the auditory neurons in DC and the central part of CN were lower(about 10dB SPL),but considerably higher in the dorsal and ventral region of EC.Results suggest that each of the divisions in the mouse IC may have different auditory functions.     

On local realism and commutativity

James D. Malley and Arthur Fine have argued that interest in noncontextual hidden variable theories is misplaced because at bottom such theories are simply attempts to turn noncommuting quantities into commuting ones. We disagree and argue that the issues are richer and more complex.     

小波神经网络的改进及应用

提出一种带有动态补偿的双隐层小波神经网络,其中过程神经元隐层完成对输入信息过程模式提取和对时间的聚合运算,小波函数代替非时变一般神经元隐层函数,用于提高网络对系统输入输出之间复杂关系的映射能力,动态补偿用于提高建模精度.将其应用于管道泄漏故障的检测,收到良好的效果.     

基于RBF神经网络在线辨识的永磁同步电机单神经元PID矢量控制

提出一种基于RBF神经网络在线辨识的永磁同步电机单神经元PID矢量控制新方法,该方法针对传统的PI调节器固定参数所造成的不足,利用具有自适应能力的单神经元PID调节器和RBF神经网络相结合,实现了参数在线辨识,转速在线控制.仿真结果表明该方法控制精度高,动态特性好,适合于永磁同步电机的速度控制.     

一维Theta神经网络微分方程模型的研究——多放电行波解的存在性

研究一维theta神经元网络的微分方程模型,对该网络模型从神经元生理运动特点和微分方程本身入手进行讨论.对模型中的输入形式加以简化,证明了对任意的传播速度c＞0,存在一个对应的耦合强度参数,使网络具有多放电行波解.     

声子之间相互作用对量子点中弱耦合激子基态能量的影响

采用线性组合算符方法研究了半导体量子点中弱耦合激子的性质.讨论了声子之间相互作用对激子基态能量的影响.数值结果表明:声子之间相互作用对激子基态能量的影响不能忽略.     

Klein-Weyl's program and the ontology of gauge and quantum systems

We distinguish two orientations in Weyl's analysis of the fundamental role played by the notion of symmetry in physics, namely an orientation inspired by Klein's Erlangen program and a phenomenological-transcendental orientation. By privileging the former to the detriment of the latter, we sketch a group(oid)-theoretical program—that we call the Klein-Weyl program—for the interpretation of both gauge theories and quantum mechanics in a single conceptual framework. This program is based on Weyl's notion of a “structure-endowed entity” equipped with a “group of automorphisms”. First, we analyze what Weyl calls the “problem of relativity” in the frameworks provided by special relativity, general relativity, and Yang-Mills theories. We argue that both general relativity and Yang-Mills theories can be understood in terms of a localization of Klein's Erlangen program: while the latter describes the group-theoretical automorphisms of a single structure (such as homogenous geometries), local gauge symmetries and the corresponding gauge fields (Ehresmann connections) can be naturally understood in terms of the groupoid-theoretical isomorphisms in a family of identical structures. Second, we argue that quantum mechanics can be understood in terms of a linearization of Klein's Erlangen program. This stance leads us to an interpretation of the fact that quantum numbers are “indices characterizing representations of groups” ((Weyl, 1931a), p.xxi) in terms of a correspondence between the ontological categories of identity and determinateness.     

基于复杂网络的信号检测与传递

近年来,关于复杂网络的研究已取得了长足的进展,且将复杂网络理论应用于其他学科的研究正方兴未艾.本文简要介绍复杂网络理论在信号检测与传递方面的一些初步研究进展,主要关注三方面的研究成果:(1)复杂网络上的信号放大;(2)与复杂网络有关的信号检测;(3)复杂网络上的自维持振荡.这些阶段性的研究成果从复杂网络的新角度加深了我们对神经元网络宏观动力学行为的微观机制的理解,并有助于刻画信息传递从神经元物理网络向脑功能网络的过渡.     

基于粗糙-模糊神经网络的焊接图像缺陷识别

针对焊接图像缺陷识别中提取的特征受噪声干扰比较严重以及现有的识别算法准确率低的问题,提出了一种基于粗糙模糊神经网络的缺陷识别算法。该算法充分利用了粗糙集的属性约简、模糊集的处理不精确数据以及神经网络的自学习、对任意函数逼近的优点,有效地解决了不确定建模过程中样本数据受到噪声干扰、模型结构难以确定的问题。仿真结果表明:该算法能有效地提高焊缝图像的缺陷识别能力。