SVM的讲义！具体的内容可以自己看！很详细的

人工智能课程的实验中用C语言环境实现子句归结的算法和反映-experimental artificial intelligence courses using C language environment to achieve phrase attributed to the algorithm and reflect

java神经网络工具箱-neural network toolbox

多重网格法在step形状区域上的实现,三种不同光滑步可以实现.-Multi-Grid Method in the step shape of the region on the realization of three different smoothing step can be achieved.

网上一直没有Harris算法的VC程序 无奈 自己写一个吧 用了两个小时就写好了 非常简陋 大家凑合着看 2005年11月15日-Harris algorithm VC procedures helpless himself wrote it with a two hour written a very simple you can make do with a November 15, 2005

一个遗传算法源程序 可以作为学习改进使用 希望对大家有所帮助-A genetic algorithm source code can be used as learning to improve the use of want to be helpful to everyone

一个BAM神经网络的基本程序，通过这个程序可以使大家对BAM有一个详细具体的了解。-A BAM neural network basic procedures, the adoption of this procedure allows us to BAM has a detailed understanding.

针对现有遗传算法在多维非线性优选方面的不足，本文提出了一种基于小生境进化算法（NEA）的非线性优选模型，探讨了NEA算法的参数选择原则。通过大量仿真和比较，表明算法在复杂非线性优选中具有快速、高效、鲁棒性强的特点，并能在全局范围内有效搜索所有最优解。 -against existing genetic algorithms in three-dimensional nonlinear optimization for the shortage, the paper presents a niche evolutionary algorithm (NEA) nonlinear optimization model, the NEA on the parameters chosen algorithm principle. Through simulation and large, the algorithm shown in a complex nonlinear optimization is fast, efficient, robust features of the strong, and the global scope effective search all the optimal solution.

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

模拟退火算法实现，内负有模拟退火的聚类，TSP等等6种问题的实现例子，在EXAMPLE文件夹内-Simulated annealing algorithm, which has a simulated annealing clustering, TSP, etc. 6 kinds of issues realize examples folder in EXAMPLE

人工智能bp算法程序以及实验报告-bp artificial intelligence algorithms and experimental procedures report