经典的BPN人工神经网络例子源码 ，很经典的

使用遗传算法、模拟退火算法求解设计结构矩阵，并进行对比分析-The use of genetic algorithms, simulated annealing algorithm design structure matrix, and a comparative analysis of

关于机器人控制，算法是可以实现的，希望可以给大家帮组-On the robot control algorithm can be achieved, I hope everyone can help Group

自己用swarm开发的一个车辆跟驰的模型，希望能跟广大爱好者共同探讨。-own use development of a swarm of vehicles- following model, the majority of fans are able to explore together.

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

一个简单灵活的数据挖掘实验平台，算法基本为空，但提供读数据，基本数据预处理功能，欢迎大家使用，共同提高。-a simple and flexible experimental data mining platform, the basic algorithm for air, but for reading data, the basic data preprocessing functions Everyone is welcome to use together.

遗传算法的机器人路径规划的源代码。用C++来实现的-Genetic algorithm path planning robot

支持向量机Windows Svm-Support Vector Machines of Windows Svm

Lin-Kernighan heuristic for the TSP and minimum weight perfect matching

MATLAB编写的BP神经网络程序，好用。-MATLAB prepared by BP neural network procedures, easy to use.

模拟退火算法：源于遗传算法，用来解决优化、决策等复杂的模型和问题。-Simulated annealing algorithm: from the genetic algorithm, to solve optimization, decision-making complex models and problems.