btws

易买网项目源码：内有购物车功能，登录，注册，订单管理，商品管理等功能(Easy to buy network project source code: there are shopping cart function, login, registration, order management, merchandise management functions)

这是一个用VC编译的小游戏程序，希望各位同学老师多多指导-This is a translation of VC small Games, I hope many teachers guide students

有助于打好c语言基础,内容描述详细易懂，适合零基础学习(Very Good Can help you quickly understand C language, improve your programming ability)

多参数描述Lotka-volterra 捕食者猎物曲线模型(Multi parameter description of Lotka-volterra predator prey curve model)

说明：  stm 32 的平衡小车初步代码，包含MPU6050(STM 32 balance car preliminary code)

delphi调用flash做为欢迎界面附flash及delphi源码-delphi call as a welcome flash interface flash attached and delphi source

说明：  课件精灵；一次注册，终身免费升级使用！ 包含服务：即时生成授权注册序列码 课件精灵V1.56 1.56主要更新： 1、修改默认背景色为白色 2、新增PDF导出功能 3、修改优化软件界面布局 丰富的多媒体互动性 互动性高与内容丰富的多媒体课程，可以吸引学员们的注意力，以达到最佳的学习效果，并可很容易地将文字、图形、动画、声音及视讯组合起来，再加上最新的动画、防锯齿文字，及拥有图像、视频、音频、动画文件的支持；多种直线曲线图形，多种笔形的支持等，将使得您目不暇接；以及内建多种不同的互动模式，使得训练变得有声有色。 提高课件的制作效率 课件精灵提供管理内、外部媒体素材的能力，让课程的编辑与管理更有效率。编辑与媒体素材的导入导出，以最简单的步骤即可完成任务，大幅节省制作的时间(Courseware Wizard: once registered, free upgrade for life! Include service: generate authorization registration sequence code in real time)

认知无线电网络在 NS2 上 caodv （认知 adhoc 的点播矢量） 路由协议的执行情况

开放的Java报告生成库。XML报告模板被用来生成准备打印的文档，这些文档来自指定的数据源，包括JDBC。输出可以传送到屏幕，或者以PDF，HTML或者XML的格式存储-open Java for report generation. XML report templates are used to create print ready files, these files from the specified data sources, including JDBC. Output can be sent to the screen, or PDF, HTML or XML format storage

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)