▍1. NARX
利用非线性神经网络做时间序列预测，主要是做动态预测。(Time series prediction using nonlinear neural network, mainly to do dynamic prediction.)
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▍2. 粒子群算法
粒子群算法及讲解，容易理解，适合新手学习数学建模(Particle swarm optimization and explanation)
▍3. 289936
用概率神经网络对桩_承台进行损伤识别_第3章概率神经网络(Use probabilistic neural network to pile _ pile caps is to identify the damage _ chapter 3 probabilistic neural network)
▍4. gailvwangluoduishouxietideshibie
基于概率神经网络的手写体数字识别,可以尝试作文字图像的辨识( the programm of neural network)
▍5. yiqunandyichuan
本科毕业设计时最后版本，蚁群和遗传算法的混合算法。(Graduation design the final version of the hybrid ant colony algorithm and genetic algorithm.)
▍6. PSO_GA_RBF
粒子群算法、遗传算法优化RBF径向基神经网络。(Particle swarm optimization, genetic algorithm optimization of RBF Radial Basis Function Neural Networks.)
▍7. GWO
灰狼算法优化FNN神经网络进行标准数据集的分类(Particle swarm optimization algorithm GWOand universal gravitation algorithm GSA combined with FNN neural network to optimize the classification of standard data sets)
▍8. Apso-bp-Rainfall
降水短期气候预测是一个非常复杂、重要的研究课题。为了提高其预测能力，拟采用1959—2011 年逐月74 项大气环流特征量序列、月平均500 hPa 高度场和月平均海温场，选取预测因子；用主分量分析方法提取样本数据中主要信息为综合因子。用粒子群优化人工神经网络方法，建立宣城市夏季降水短期气候预测模型。对2007—2011 年宣城市夏季降水预报检验结果表明，粒子群优化人工神经网络收敛速度快，迭代次数少；试报平均绝对误差是66.5 mm，绝对值平均相对误差10.5 ，预测精度高，具有很好的应用推广前景。 (Precipitation of short-term climate prediction is a very complex and important research topic. Intends to adopt in order to improve its ability to predict the the 1959-2011 monthly 74 atmospheric circulation feature series, monthly mean 500 hPa height field and monthly average sea surface temperature field, select the predictor extract the sample data using principal component analysis for the Synthesis factor. Artificial neural network using particle swarm optimization method, Xuancheng City in summer rainfall in short-term climate prediction model. 2007-2011 declared the city in summer precipitation forecast verification results show that the particle swarm optimization artificial neural network convergence speed, fewer iterations trial reported an average absolute error is 66.5 mm, the absolute value of the average relative error of 10.5 , high prediction accuracy, good application prospect.)
▍9. RBF
径向基神经网络（RBF网络）的三种学习算法实现：随机选取中心法、自组织选取中心和梯度训练算法(Three radial basis function neural network (RBF) learning algorithm: randomly selected center method, self-organizing selection center and gradient training algorithm)
▍10. pso_wavelet
小波神经网络模型，采用粒子群算法优化，包含单输入、多输入(Wavelet neural network model, using particle swarm optimization, including single-input, multiple-input)
▍11. fga
标准的遗传算法程序源代码，解释很全，适合初学者，经过自己调试通过(Standard genetic algorithm source code, to explain it all, suitable for beginners, through their commissioning by)
▍12. msd
这是对多符号序列检测解调的matlab程序说明，能有效提高解调性能，增加传输距离，有利于后续的研究。(This is a matlab program for multi symbol sequence detection and demodulation. It can effectively improve the demodulation performance and increase the transmission distance, which is conducive to subsequent research.)
▍14. BP-PID
说明： 利用BP神经网络优化PID控制器参数，实现在线整定，达到最优化。(The parameters of PID controller are optimized by BP neural network to realize on-line tuning and optimization.)
▍15. CROlib.mat_1.0.2
化学反应优化算法 最近提出的一种优化算法 源代码(Chemical Reaction Optimization )
▍16. MOPSO-matlab
多目标粒子群优化算法的matlab版本。最经典的多目标进化算法！ MOPSO(Multi-objective particle swarm optimization algorithm using matlab. MOPSO)
▍17. clustering
基于快速搜索数据密度峰值的聚类算法是一种基于聚类中心具有较近邻点有更高密度且其与更高密度点间有着较大的相对距离的一类算法。(Clustering by fast search and find of density peaks is based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively large distance points with higher densities.)
▍18. Iris
模式识别,对iris数据进行分类,svm算法(Pattern recognition, classification of iris data, svm algorithm)
▍19. lyapunov(wolf)
混沌时间序列中的用wolf法计算lyapunov 指数，速度超快(In chaotic time series using wolf method in the calculation of Lyapunov index, speed Ultrafast )
▍20. main
说明： 遗传算法，用于计算车辆调度问题，解决优化问题(Using generation method)
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