OPenCV written using a hough transform line detection procedure

模糊pid的一个例子，在Visual c++下通过-An example of fuzzy pid, in the Visual c++ Through

ISODATA 算法的 C++ 语言的实现源码-ISODATA algorithm C++ Realize language source

基于有限状态机的汉语数字语音端点检测.rar.rar格式为ｖｉｐ-based on the finite state machine language digital voice endpoint detection. Rar.rar format vip

由于K－均值聚类算法局部最优的特点，而模拟退火算法理论上具有全局最优的特点。因此，用模拟退火算法对聚类进行了改进。20组聚类仿真表明，平均每次对K结果值改进8次左右，效果显著。下一步工作：实际上在高温区随机生成邻域是个组合爆炸问题（见本人上载软件‘k－均值聚类算法’所述），高温跳出局部解的概率几乎为0，因此正考虑采用凸包约束进行模拟聚类，相关工作正在进行。很快将奉献给各位朋友。-as K-means clustering algorithm for optimal local characteristics, and simulated annealing algorithm theory with the characteristics of the global optimum. Thus, simulated annealing algorithm for clustering improvements. Cluster Group of 20 simulations show that the average value of K results improved about eight times, the results are obvious. The next step : In fact, in high temperature generated random neighborhood is a combination of explosives (see my software on the "k-means clustering algorithm" mentioned above), high-temperature solution of partial out almost zero probability, it is considering the use of convex hull bound for simulation cluster, the work under way . Soon dedication to the ladies.

扩展的Infomax算法，具有比较快的收敛速度！而且程序非常简洁,明了-expansion of Infomax algorithm, with a faster rate of convergence! The procedure is very simple, straightforward

量子遗传算法包含比较详细的算法说明，而且应用于滤波器的设计- quantum genetic algorithm

Fuzzy Rule Base Implamentation

GCP(图着色问题)的模拟退火算法应用源码-GCP (Graph Coloring Problem) application of the simulated annealing algorithm source code

利用SOM（Self-Organization Map）数据聚类方法，包含点数据集的坐标，并以TChart分组为例进行说明。

LIBSVM源码。LIBSVM 是台湾大学林智仁(Chih-Jen Lin)博士等开发设计的一个操作简单、 易于使用、快速有效的通用SVM 软件包，可以解决分类问题（包括C- SVC、 n - SVC ）、回归问题（包括e - SVR、n - SVR ）以及分布估计（one-class-SVM ） 等问题，提供了线性、多项式、径向基和S形函数四种常用的核函数供选择，可以有效地解决多类问题、交叉验证选择参数、对不平衡样本加权、多类问题的概率估计等。-LIBSVM source. LIBSVM is林智仁Taiwan University (Chih-Jen Lin) Dr. develop design a simple, easy to use, fast and effective generic SVM software package, can solve the classification problems (including the C-SVC, n- SVC), regression ( including e- SVR, n- SVR) as well as the distribution of estimates (one-class-SVM) and so on, provides a linear, polynomial, radial basis function and the S-shaped kernel function of four commonly used for selection, can effectively to solve a wide range of issues, cross-validation to choose the parameters of the imbalance in the weighted sample, multi-category probability estimation.