该程序是遗传算法的具体实现，通过该程序可以对遗传算法有更深入的了解。...

该程序代码实现BP网络函数逼近，适合初学者学习-The program code to achieve BP network function approximation, suitable for beginners to learn

模糊控制逻辑飞机着陆问题算法的vc编程实现-Fuzzy logic control of the aircraft landing problem vc Programming Algorithm

K-均值聚类算法的编程实现。包括逐点聚类和批处理聚类。K－均值聚类的的时间复杂度是n*k*m，其中n为样本数，k为类别数，m为样本维数。这个时间复杂度是相当客观的。因为如果用每秒10亿次的计算机对50个样本采用穷举法分两类，寻找最优，列举一遍约66.7天，分成3类，则要约3500万年。针对算法局部最优的缺点，本人正在编制模拟退火程序进行改进。希望及早奉给大家，倾听高手教诲。-K-means clustering algorithm programming. Point by point, including clustering and clustering batch. K-means clustering of the time complexity of n* k* m, n samples, several types of k, m sample dimension. The time complexity is a very objective. Because if we use one billion times per second the computer using 50 samples of two exhaustive method to find the optimal set out again about 66.7 days, divided into three categories, offering 3,500 years. Local optimal algorithm against the shortcomings, I was prepared simulated annealing process improvements. Early Feng hope for everyone, listen to the master teachings.

也是学习别人的程序，不过他没有把核心代码给我-also learn from others procedures, but he did not take me to the core code

分布估计算法（EDA）讲义，29页PPT 1. 从GA到 EDA 2. 链接学习与模型 3. 连续EDA 4. EDA 与玻耳兹曼分布 5. 资源 -estimation of distribution algorithm（EDA）tutorial 1. from the GA to the EDA 2. Linking learning and models 3. Continuous EDA 4. EDA with Boltzmann distribution 5. other Resources

本程序用模拟退火算法实现了旅行商问题（tsp问题）-the procedures used simulated annealing algorithm to achieve the traveling salesman problem (tsp)

基与多种智能方法和模式识别技术的研究,如果你也在从事相关研究,一定要看看,我想应该有借鉴价值-base with a variety of intelligent methods and pattern recognition techniques, but if you are engaged in related research, we must look at, I think there should be a reference value

这是图象处理模式识别一书的源代码，包括C++和MATLAB的程序，对于做这方面的研究人员来说，会很有帮助的。-This is an image processing pattern recognition on the source code, including C and Matlab procedures for doing this kind of research personnel, will be very helpful.

粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation).源于对鸟群捕食的行为研究 PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-Particle Swarm Optimization (PSO) is an evolutionary technology (evolutionary computation). Predatory birds originated from the research PSO with similar genetic algorithm is based on iterative optimization tools. Initialize the system for a group of random solutions, through iterative search for the optimal values. However, there is no genetic algorithm with the cross- (crossover) and the variation (mutation). But particles in the solution space following the optimal particle search. The steps detailed chapter on the future of genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and ot

BP网络解决异或问题的实例，有初始值和最终结果的显示。本网络结构为三层BP网络，各层神经元个数可变，更改相关#define即可。（注释较详细，主函数中调用的函数和矩阵类都定义在文件function.h中）-BP network to solve XOR problem instance, there are initial values and final results are displayed. The BP network structure for the three-tier network of neurons in the number of floors variable, change the relevant# Define you. (Note more detailed, the main function calls the function and matrices are defined in the document in function.h)