matlab-code-BPNN

说明：  数据统计描述 第一节 数据分析中常用的统计量及其分布 第二节 频数变量数据特征描述 第三节 综述变量数据特征的描述 第四节 数据分析过程的记录与重现(Data described in section I of data analysis commonly used in statistics and their distribution in section II, the frequency characteristics of variable data variables described in Section III Summary data on the characteristics of the process of data analysis described in section IV of the record and reproduce)

C++Builder与Matlab7_1混合编程环境设置.(C++ Builder programming environment with mixed Matlab7_1 settings.)

刷钻的大家用用挺好的用的呵呵 你们谩骂用吧(Brush with a very good drill for you to use the abusive use of Oh you use it)

混合罚函数法，结合了外罚函数法和内罚函数法的有点，适用于matlab程序(Mixed penalty function method, a combination of external and internal penalty function method a little penalty function method for matlab program)

求解码垛机器人的运动空间，并对空间点云求取其边界。最后对边界进行排序。(Solving palletizing robot movement space, and space point cloud to strike its borders. Last Sort boundaries.)

风电场双馈异步电机仿真，希望对大家有所帮助(Doubly-fed induction motor wind farm simulation, we hope to help)

一个关于SVM支持向量机的计算例程, 用MATLAB做的,请多指教(one of the SVM SVM calculation routines, using MATLAB do, please enlighten)

FHSS_DSSS_CDMA的m文件，可以在matlab的simulink上仿真~(FHSS_DSSS_CDMA the m file )

（1） 本系统用到四种神经网络对漏电嫌疑系数进行预测，分别是BP神经网络、RBF神经网络、GRNN神经网络、Elman神经网络，通过对神经网络结构参数的设置和调试，可以得到很好的预测效果，实际值和预测值之间的误差能达到5 以内。 （2） 本系统使用matlab做GUI界面，在界面上就可以分别对这四种神经网络的结构参数进行设置，从而可以调整神经网络的结构，达到较好的预测精度。 ((1) The system used four neural network to predict the suspected leakage coefficient, respectively, the BP neural network, RBF neural network, GRNN neural network, Elman neural network, the neural network structure parameter setting and commissioning, can get very good prediction, between the actual and predicted values ​ ​ of error of less than 5 . (2) the use matlab to do the GUI interface, the interface can each of these four neural network structure parameters set, the structure of the neural network, which can be adjusted to achieve better prediction accuracy.)

MachineLearning Classifican KNN-Based Clustering for Improving Social Recommender Systems