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说明：  ppyy的一个文件，作为介绍入门，其中的代码是非常简洁的(A file of ppyy as an introduction to introduction)

基于jQuery HTML5实现的一些网页菜单，这些菜单看上去很有美感，而且制作思路也是很新鲜，主要是使用网页上的那些特殊符号、字符来实现的，一共有10款这种风格的菜单，个别还带有动画效果，这些菜单若在支持HTML5的浏览器中运行，动画更流畅，效果更佳。

javascript教程上部分-javascript partly Guide

此程序的功能是，采用细胞自动机方法，进你火灾模拟。(This program' s function is, using cellular automata method, into your fire simulation.)

说明：  UDS协议栈中文版本，英文版看不懂的时候对照学习比较好，建议还是以英文版为主(UDS protocol stack Chinese version, English version can not understand when the control learning is better, it is recommended that the English version)

学生学籍管理系统,功能齐全，内容丰富，赶紧下载-xueshengxuejiguanlixitong

labview 写的拼图游戏，挺好玩的,华容道-labview game

说明：  利用cvx解半定规划问题，得到波束成形矩阵。(The semidefinite programming problem is solved by CVX)

说明：  可以实现最近对问题 分治法的求解 程序已经验证(Recent divide-and-conquer method for problem solving is implemented, and code detection is no problem)

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)