areps

说明：  matlab写的，把三张照片拼在一起,变成一个大的完整的，三张照片需要能拼接(Matlab wrote, put together three photos, into a large complete, three photos need to be able to stitch together)

Hacker PC, Exploit windows dektop and phshins

python 书籍 非常经典，希望大家有所收获(python very classic books, I hope you all have a harvest)

适用于初学CST的初学者！！！！！！！！！！！！！(Suitable for beginners of CST)

This complete application (including login authentication, online status indicator, new message audio prompt, online status change audio prompt) allows you to integrate a realtime chatroom into your .asp application without the need for applets. Apologies for first upload probs...not my fault honest ! Edit the included users.xml file to define user and admin accounts. see the included readme.txt for detailed instructions on install and configuration.

asn.1编码的文件查看器，非常不错，是一个不错的小工具-asn.1 coded document viewer, very Yes, it is a good tool for small

说明：  123.zip which show information of 6

说明：  笔者设计了求解广义逆的程序，可以求满列秩矩阵的右广义逆。(The author designs a program to solve the generalized inverse)

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)

Wz_EventServer para Mu 075