优化

一个老外的开源论坛,使用ASP.NET 3.5开发,SQL SERVER 2005数据库,可以学习或者直接使用-A foreigner" s open-source forum, use the ASP.NET 3.5 development, SQL SERVER 2005 database, you can learn or be used directly

说明：  friction lagrang sabrotin link abaqus

含有移相器的配电网潮流计算程序，非常适用，我们团队自已开发并一直在使用。-Contains a phase shifter distribution power flow calculation program, is very applicable to our own development team and has been in use.

此源码是简单Pascal编译器实现代码，采用工具LEX和YACC实现(this source is simple Pascal compiler for code using tools LEX and YACC)

说明：  点阵字体库,分别是12点阵，16点阵，64点阵(Dot matrix font library, 12 dot matrix, 16 dot matrix, 64 dot matrix, respectively)

说明：  实现孕妇和婴儿心电信号的检测并分离孕妇婴儿的心电信号(Realize the detection of the ECG signals of pregnant women and babies and separate the ECG signals of pregnant women and babies)

说明：  D star 规划算法 可用 哈哈 希望大家能够一起学习进步(D star programming algorithm available ha ha hope you can learn and progress together)

复杂网络仿真软件pajek关于引文网的仿真程序。-Complex network simulation software on the citation network pajek simulation program.

自己做的数字逻辑电路课程设计，课题：八位二进制并行加法器的实现，包含代码和流程图以及基本说明-Themselves to do the digital logic circuit design, topics: 8 parallel binary adder realize that contains code and flow chart as well as basic instructions

说明：  独立成分分析(Independent Component Analysis，ICA)是近年来提出的非常有效的数据分析工具，它主要用来从混合数据中提取出原始的独立信号。它作为信号分离的一种有效方法而受到广泛的关注。近几年出现了一种快速ICA算法(Fast ICA)，该算法是基于定点递推算法得到的，它对任何类型的数据都适用，同时它的存在对运用ICA分析高维的数据成为可能。又称固定点(Fixed-Point)算法，是由芬兰赫尔辛基大学Hyvärinen等人提出来的。是一种快速寻优迭代算法，与普通的神经网络算法不同的是这种算法采用了批处理的方式，即在每一步迭代中有大量的样本数据参与运算。但是从分布式并行处理的观点看该算法仍可称之为是一种神经网络算法。(Independent component analysis (ICA) is a very effective data analysis tool proposed in recent years. It is mainly used to extract the original independent signals from the mixed data. As an effective method of signal separation, it has been widely concerned. In recent years, a fast ICA algorithm (fast ICA) has appeared. The algorithm is based on the fixed-point recursive algorithm, which is applicable to any type of data. At the same time, its existence makes it possible to use ICA to analyze high-dimensional data. Also known as fixed-point algorithm, it was proposed by HYV & auml; rinen et al. It is a fast optimization iterative algorithm. Different from the common neural network algorithm, this algorithm adopts the way of batch processing, that is, there are a large number of sample data in each iteration. But from the point of view of distributed parallel processing, this algorithm can still be called a neural network algorithm.)