▍1. ELM_MultiOutputRegression
黄广斌官网下载,多输出回归问题的程序，可做参考(extrem learnig machine multiouptut regression)
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▍2. 遗传算法优化BP神经网络预测
说明： 用遗传算法为 BP 神经网络优化权值，使网络具有快速学习网络权重的能力，并且能够摆脱局部极小点的困扰。遗传算法的全局搜索能力来弥补BP算法全局搜索能力不足，实例证明，这种预测模型比BP网络预测模型具有更高的精度。(Genetic algorithm is used to optimize the weights of BP neural network, so that the network has the ability to quickly learn the network weight, and can get rid of the trouble of local minimum points. The global search ability of genetic algorithm is used to make up for the deficiency of BP algorithm. The example shows that this prediction model has higher accuracy than BP network prediction model.)
▍3. Fuzzy-Neural-Network-by-matlab
这是一个四个不同的S函数实现集合的递归模糊神经网络（RFNN）。该网络采用了4组可调参数,这使得它非常适合在线学习/操作，从而可应用到系统识别等方面。(This is a collection of four different S-function implementations of the recurrent fuzzy neural network (RFNN) described in detail in [1]. It is a four-layer, neuro-fuzzy network trained exclusively by error backpropagation at layers 2 and 4. The network employs 4 sets of adjustable parameters. In Layer 2: mean[i,j], sigma[i,j] and Theta[i,j] and in Layer 4: Weights w4[m,j]. The network uses considerably less adjustable parameters than ANFIS/CANFIS and therefore, its training is generally faster. This makes it ideal for on-line learning/operation. Also, its approximating/mapping power is increased due to the employment of dynamic elements within Layer 2. Scatter-type and Grid-type methods are selected for input space partitioning.)
▍4. Fuzzy-Neural-Network-by-matlab
这是一个四个不同的S函数实现集合的递归模糊神经网络（RFNN）。该网络采用了4组可调参数,这使得它非常适合在线学习/操作，从而可应用到系统识别等方面。(This is a collection of four different S-function implementations of the recurrent fuzzy neural network (RFNN) described in detail in [1]. It is a four-layer, neuro-fuzzy network trained exclusively by error backpropagation at layers 2 and 4. The network employs 4 sets of adjustable parameters. In Layer 2: mean[i,j], sigma[i,j] and Theta[i,j] and in Layer 4: Weights w4[m,j]. The network uses considerably less adjustable parameters than ANFIS/CANFIS and therefore, its training is generally faster. This makes it ideal for on-line learning/operation. Also, its approximating/mapping power is increased due to the employment of dynamic elements within Layer 2. Scatter-type and Grid-type methods are selected for input space partitioning.)
▍5. FNN
基于T-S的模糊神经网络，直接用于预测，预测效果非常好，这是我写论文的程序，欢迎下载(Based on t-s fuzzy neural network, directly used in prediction, prediction effect is very good, this is I write a paper application, welcome to download)
▍6. GARCH
说明： GARCH建模实例讲解。一个完整的程序示例，以供学习。(GARCH model)
▍7. SLLE
引入数据类别信息的有监督局部线性嵌入算法，可用于数据分类(Supervised Locally Linear Embedding)
▍8. SS-US-ELM
最新极限学习机程序,绝对可以使用，请大家下线载(G. Huang, S. Song, J. N. D. Gupta, and C. Wu, “Semi-supervised and Unsupervised Extreme Learning Machines,” (in press) IEEE Transactions on Cybernetics, 2014.)
▍9. 象棋识别
工作是棋盘图像二值化和棋子识别。针对棋盘全局二值化存在的问题,提出了基于相邻像素灰度差阈值的棋盘图像二值化方法。(The work is to binarize the chessboard image and recognize the chessboard. Aiming at the problem of global binarization of chessboard, a method of binarization of chessboard image based on gray difference threshold of adjacent pixels is proposed.)
▍10. 象棋识别
说明： 工作是棋盘图像二值化和棋子识别。针对棋盘全局二值化存在的问题,提出了基于相邻像素灰度差阈值的棋盘图像二值化方法。(The work is to binarize the chessboard image and recognize the chessboard. Aiming at the problem of global binarization of chessboard, a method of binarization of chessboard image based on gray difference threshold of adjacent pixels is proposed.)
▍11. RBF_control
以卫星编队动力学模型为对象，利用RBF神经网络算法进行控制，包括卫星轨道控制和姿态控制(Satellite fleet dynamics model as an object, use RBF neural network algorithm control, including satellite orbit control and attitude control)
▍12. 23.Elman神经网络预测数据
说明： Elman神经网络预测数据,matlab平台，稀缺资源(Elman neural network prediction data, Matlab platform, scarce resources)
▍13. 61 遗传算法设计PID控制器
说明： matlab优化算法学习，实例，遗传算法设计PID控制器(MATLAB optimization algorithm learning, examples, Genetic Algorithm Design PID controller)
▍14. 51 模糊神经网络算法实现水质评价
说明： 模糊神经网络算法实现水质评价，里边有代码，适合学习，推荐(Fuzzy neural network algorithm to achieve water quality evaluation, there are codes, suitable for learning, recommendation)
▍15. 50 模糊控制PID控制器
说明： 优化算法matlab，适合初学者，很详细(Optimization algorithm matlab, suitable for beginners, very detailed)
▍16. PID_Fuzzy
传统PID控制和自适应模糊控制的仿真对比(Traditional PID control and adaptive fuzzy control simulation comparison)
▍17. The-QPSO-
QPSO量子粒子群算法,QPSO LSSVM,SVM(The QPSO quantum particle swarm optimization)
▍18. ga-wind-prediction
运用GA遗传算法优化BP网络，对风电功率进行预测，含实际数据和案例。(GA genetic algorithm to optimize the use of BP network prediction for wind power, with actual data and case studies.)
▍19. ga-wind-prediction
运用GA遗传算法优化BP网络，对风电功率进行预测，含实际数据和案例。(GA genetic algorithm to optimize the use of BP network prediction for wind power, with actual data and case studies.)
▍20. GA-BPANN
说明： 经过遗传算法优化的BP神经网络程序GA-BPANN(After a genetic algorithm BP neural network program GA-BPANN)
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