nsct_toolbox

MATLAB 7.0从入门到精通，求是科技，人民邮电出版社(MATLAB 7.0 from entry to the master, seeking technology, Posts & Telecom Press)

该程序提供了多种参数的双指数脉冲函数的波形及其频谱分解图，要注意的是作图时坐标比例尺的调整。程序中给出的是1.2/50μs和0.7/60ns两种波形可直接使用的坐标比例尺。 在MATLAB5.0下就能工作。 (The program provides a wide range of parameters of double-exponential pulse function waveform and its spectral decomposition diagram, it should be noted that when mapping coordinates scale adjustments. Procedure is given in 1.2/50μs and 0.7/60ns two waveforms can be directly used the coordinates of scale. Will be able to work under in MATLAB5.0.)

，将电容器容量分解为TCSC和FSC两个部分， 建立了TCSC+FSC抑制SSR特性的仿真模型。对TCSC+FSC抑制不同模 式SSR的效果和不同触发角下TCSC+FSC抑制SSR的特性分别进行了仿真(To establish a TCSC+FSC inhibits SSR simulation model. Inhibition of different modes of TCSC+FSC Type SSR effect and different firing angle under TCSC+FSC SSR inhibiting properties were simulated )

学习matlab的好资料第3部分，再此共享下啦。参考参考(Good information to learn matlab Part 3, then under this shared friends. Reference Reference)

实现希尔伯特-黄变换的功能，并带有一段测试数据。希尔伯特-黄变换是使用EMD分解将信号分解为本征模态函数（IMF），然后求得IMF分量的瞬时频率。其对于非稳态和非线性信号具有优良的效果。(The function plot_hht is a realization of the Hilbert-Huang transform (HHT). The HHT decomposes a signal into intrinsic mode functions (or IMFs), and obtain the instantaneous frequency data. It is designed to work well for data that are nonstationary and nonlinear.)

这是code使用说明,对大家比较有帮助，新手必备(This is the code for use)

模型预测控制中的预测控制，GPC的一种，还可以，希望有用(Model predictive control of predictive control, a GPC, and also, I hope useful)

最优决策——用几个matlab编程的实例阐述分支定界法的思想是：首先确定目标值的上下界(Optimal decision-making- matlab programming with some examples of the branch and bound method is the idea: First, determine the target of the upper and lower bounds)

基于MATLAB的信号均衡器与分离器的程序(MATLAB signal equalizer separator-based programs)

用于分类的主判别变量算法(principal discriminant variate algorithm) ( principal discriminant variate algorithm for two classes, x1 and x2. x1 is the training samples in the 1st class with each column being the spectrum characterizing the corresponding sample. x2 is the training samples in the 2nd class with each column being the spectrum characterizing the corresponding sample. lamda is the penalty weight defining the balance between FLDA and PCA. dvnum is the number of principal discriminant variates to be computed. A defaul value for dvnum is 2. dv is the principal discriminant variates. scores is the matrix with each row being the scores of the samples on the corresponding principal discriminant variate. xmean is the mean spectra of the sample, which was used for mean-centering the data in training and prediction. )