salientandnonsalientpolesynchronousgenerator

jQuery的1.6.4版本的js文件，非常方便，欢迎品尝(Document editor, can open various types of text files, very convenient. Strongly recommended)

说明：  此系统由VC++ 6 0 MFC开发而成,主要实现一些基本的界面操作,如画笔画刷,文字改变字体颜色,以及时钟显示和小型购物系(This system is developed by VC 60 MFC. It mainly implements some basic interface operation, such as brush, text changing font color, clock display and small shopping department.)

说明：  K近邻算法。(k-Nearest Neighbor，KNN)，是一个理论上比较成熟的方法，也是最简单的机器学习算法之一。该方法的思路是：在特征空间中，如果一个样本附近的k个最近(即特征空间中最邻近)样本的大多数属于某一个类别，则该样本也属于这个类别。(K-nearest neighbor algorithm. K-nearest neighbor (KNN) is a mature method in theory and one of the simplest machine learning algorithms. The idea of this method is: in the feature space, if most of the k nearest (i.e. the nearest) samples near a sample belong to a certain category, then the sample also belongs to this category.)

说明：  实现邓肯-张本构模型二次开发的Fortran程序(Fortran Program for the Second Development of Duncan-Zhang Constitutive Model)

人物 楚留香 演示游戏下怎么实现人物走动 里面有详细文档说明 开发平台vs2008+hge游戏引擎 以前做的课程设计的附带小程序-Chu Liu Xiang figures demonstrate how the achievement of the next game figures to move inside the document explains in detail the development of a platform game engine vs2008+ hge curriculum design done before the spin-off of small procedures

SRL. SCAR的基本资源库!!!没有的人可下载-SRL. SCAR of the basic resources! ! ! No person may download!

power electronics and drives simulation

说明：  基于阵列的信号增强算法，利用多个阵元接收信号进行相关计算，输出纯净语音信号。(Array-based signal enhancement algorithm)

说明：  为了提升配网供电可靠性的预测精度!提出了基于主成分分析和粒子群优化极限学习机的配网供电可靠 性预测模型$ 从多方面分析影响供电可靠性的指标!利用主成分分析得到综合变量!实现对数据的降维$ 在此基 础上!构建人工神经网络并利用粒子群算法优化极限学习机的输入权值和阈值!完成对训练供电可靠性预测模型 的训练$ 以某大型电网的 ?L 个供电局样本 !% 种影响供电可靠性因素为例进行仿真分析!并将 E S R C E FQ C 4 G D算 法与 ! 种回归拟合算法对比!验证了该方法的有效性(It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms. Unlike these conventional implementations, this paper proposes a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. The experimental results based on a few artificial and real benchmark function approximation and classification problems including very large complex applications show that the new algorithm can p)

tj大学研究生自动化专业英语电子教案，刚接触自动化领域的，以及考研或者考博在该校相关领域的，都可以参考。- automation