test1.part08

Android SQLite数据库相关实例源码，代码中的每一个intent都会带一个Bundle型的extras数据。需要对position和id进行一个很好的区分。position指的是点击的这个ViewItem在当前ListView中的位置，每一个和ViewItem绑定的数据，肯定都有一个id，通过这个id可以找到那条数据。

Php使用技巧集 文件格式 chm 文件大小：61K()

网络编程的客户端,实现与服务器通信，连接状态显示(c# .net client Network programming client, to achieve communication with the server, connection status display)

说明：  优化客户端MOD,可以减少卡顿，增加用户体验(Optimizing client MOD can reduce Carton and increase user experience)

说明：  数据同化的过程，应用于天气预报或者其他模型更新，提高模型的泛化能力。(The process of data assimilation is applied to weather forecast or other model updating to improve the generalization ability of the model.)

jsp图书管理系统-jsp library management system

实现虚拟示波器的功能-Realize the function of the virtual oscilloscope

整站程序,下载网站 flash版-entire stations, flash version download site

说明：  Teacher team management: manage the information of training teachers, add pictures, modify, classify and manage Potential development management: potential development information management, which can be added, modified and other related operations Enrollment management: release employment enrollment information, which can be managed, such as modification and deletion

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)