人工智能的人工智能门户网站，很难找到，美国..

一个关于模式识别问题的很好的例子，可以对数组进行检验，非常好用！-a pattern recognition problem on the very good example of the array can be tested very handy!

遗传算法-Genetic algorithms

此程序为训练RBF网权值的量子粒子群优化算法，供各位参考-this program for training RBF net weights Quantum Particle Swarm Optimization Algorithm for reference

将遗传算法(GA)与传统SVM算法结合,构造出一种参数最优的进化SVM(GA2SVM),SVM 模型采用径向基函数(RBF)作为核函数，利用格雷码编码方式对SVM算法的模型参数进行遗传编码和优化搜索，将搜索到的优化结果作为SVM 的最终模型参数。-Genetic algorithm (GA) combined with the traditional SVM algorithm, a kind of tectonic evolution of the optimal parameters of SVM (GA2SVM), SVM model using Radial Basis Function (RBF) as kernel function, the use of Gray code encoding algorithm of the SVM model parameters of genetic coding and optimization of search, will search for the optimal results as the final SVM model parameters.

利用svm结合向量机实现蛋白质相互作用预测-Svm vector machine achieved with the use of protein-protein interaction prediction

该程序包包含三个函数，一个用于建立BP神经网络，一个用于训练该网络，最后一个用于测试样本。-The package contains three functions, one for the establishment of BP neural networks, one for training the network, the last one for the test samples.

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.

k-means算法 自定义数据样本和类中心 已经调试实现 并且有图清晰表示-k-means algorithm for custom data types of samples and testing center has been and realize that has a clear plan

模式识别k_means聚类算法。-kmeans clutering algorithm for Pattern Recognition