凝聚层次聚类的matlab代码

本程序是对V-BLAST系统及其检测算法的仿真，可采用BPSK,QPSK,16QAM,64QAM调制。检测算法为ML，MMSE,ZF,以及采用迫零的连续干扰消除检测算法。(This procedure is the V-BLAST detection algorithm and the simulation system can be used BPSK, QPSK, 16QAM, 64QAM modulation. Detection algorithm for the ML, MMSE, ZF, and the use of zero-forcing successive interference cancellation detection algorithm.)

esprit的ls算法matlab仿真，信噪比为0，生成随阵元数变化而产生均方差的变化图像。(The esprit ls algorithm Matlab simulation, and signal to noise ratio is 0, generated to produce the image of the mean square deviation of the change with changes in the number of array elements. )

What follows are example usages of polyfitn, polyvaln, poly2sympoly, polyn2sym

利用MATLAB程序中的inline和nlinfit两个命令对血药浓度-时间数据进行非线性拟合,求解血管外给药的药动学参数。同时与3P97软件计算结果进行了比较,结果表明,基于MATLAB程序可方便快速地求解血管外给药的各个药动学参数,计算结果与3P97软件计算值相当,且本方法操作简便、界面直观、易于推广,可用于临床教学或实验中药动学参数的计算。 更多还原 (Using MATLAB program inline and nlinfit two commands to the plasma concentration- time data by nonlinear fitting, solving the pharmacokinetic parameters of extravascular administration. Simultaneously with the 3P97 software to calculate the results were compared, the results show that can quickly and easily solve extravascular administration of various pharmacokinetic parameters based on MATLAB, the results calculated with 3P97 software fairly, and that the method is simple, intuitive interface, easy to spread, can be used to calculate the clinical teaching or experimental medicine pharmacokinetic parameters. More Restore)

arma模型预测的matlab代码 很完整 有注释 适合新人(ARMA model prediction matlab code is complete, annotated, suitable for newcomers)

获取windows和system32的默认完整路径，初学者可以学习参考一下(Access to windows and system32 default the full path, beginners can learn to refer to)

详细的介绍了有关matlab绘图方便的操作，对初学者非常有用(For details about the matlab graphics and convenient operation is very useful for beginners)

说明：  用Matlab进行可视化界面设计，有菜单、控件、用户输入等(using Matlab for visual interface design, menus, controls, such as user input)

BP神经网络作为若分类器，反复训练BP神经网络预测样本输出(As if the BP neural network classifier, repeated training samples of BP neural network output)

this article illustrate how to use pattern recognition tool boxes in matlab