Standard particle swarm optimization algorithm, visual c++. Net code to prepare...

MATLAB 下的模式识别特征提取 用于对特征信号的提取-MATLAB under the feature extraction for pattern recognition features of the signal extraction

随机神经网络工具箱-random neural network toolbox

猜数字游戏的小AI,有一定的准确程度,有兴趣的人可以-tones of the small game AI, a certain degree of accuracy, interested people can s

隐马尔可夫模型算法(解压后为ppt文件，改名为a.ppt)-HMM algorithm (decompression after ppt documents, renamed a.ppt)

测试无约束优化定理求解最小值,包括测试数据,在VC下可以实现,已经调试成功.-test unconstrained optimization for minimum theorem, including test data, the VC can be achieved, debugging has been successful.

GENNET是一个通过遗传算法优化权重的16个神经元的全连接神经网络。训练目的是使网络在一定假设的激励下，随时间产生同目标函数同样的响应，即:可以把此网络制作为一个任意波形的信号发生器。 程序分3个示例说明遗传算法进化过程中网络输出的变化趋势。可以看出网络输出（蓝色曲线）随着时间变化逐渐逼近目标函数（红色曲线）。此程序不仅可以学习遗传算法，也可以用于研究全连接网络的直观示例。-GENNET is a genetic algorithm to optimize the weights by the 16 neurons fully connected neural networks. Training purposes is to enable network inspired by certain assumptions, over time, produce the same response with the objective function, namely: the network can be produced as an arbitrary waveform signal generator. Procedure, three examples illustrate the evolution of genetic algorithms in the network output trends. We can see that the network output (blue curve) changes over time, gradually approaching the objective function (red curve). This program can not only learn from the genetic algorithm can also be used to study visual example of a fully connected network.

对著名的UCI机器学习库的wine数据集做的朴素贝叶斯分类算法，包含详细源代码和实验报告，不可多得啊-Pairs of well-known UCI machine learning repository data set to do wine Naive Bayesian classification algorithm, with detailed source code and test reports, a rare ah

用BP神经网络逼近函数Z=SIN(X*Y),采用C++编程.-Using BP neural network to approach the function Z = SIN (X* Y), using C++ programming.

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)

遗传算法的matlab实现，举例说明遗传算法的基本实现步骤，适用于初学者-Matlab genetic algorithm to achieve, for example, shows the basic implementation steps of genetic algorithm is suitable for beginners